{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path,'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() # 查看数据集的简单描述，得到每个属性类型和非空值数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_bedrooms为20433，说明其中有一部分非空值\n",
    "housing['ocean_proximity'].value_counts() # 查看多少种分类\n",
    "# 共有5种分类，每种分类下有多少数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()  # 显示数值属性摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from jupyterthemes import jtplot\n",
    "#jtplot.style() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIgAAANbCAYAAAApIaTMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde5xkZX3v+88XB4RAHJHRhozI6GFDdnQg0dFDAmybUSYYLkZNclQIIsbJdsf7RENM9GASskFDDF6imXjBIInZAT0oEyMw0CreEOJm2DFBwQzbMYgM5ICDBjPh2X+s1dJdUz29uqeq67I+79erX73qt1at+j1V3fVU/daznpVSCpIkSZIkSWqvvQadgCRJkiRJkgbLApEkSZIkSVLLWSCSJEmSJElqOQtEkiRJkiRJLWeBSJIkSZIkqeUsEEmSJEmSJLWcBSINVJLJJFMDeNyyiPusSrJ1N+vPSnLxnuQlSeNmKd7nkxyb5Ip+PsZidOs3kvx9kkMHlJIkaRGSnJvk3D3cxwuTvKtHKQ3se5TGmwUiSZI00kopny+lPHfQeTRRSnlqKeVbg85DktQfcx2ILqV8tJTyqi7bTyWZ7HtiUgMWiCRJkiRJklrOApGGQpILk/xrks8m2S/JuiS3Jvl2kjfV28waqj9zqGeSU5PcnuSeJH+eJHX8GfVw/u8k2Tgdr9e9qN7/N5M8uY49Kclnktyd5CNJ9p8n72PqPG8HTuqI/0OdzxVJ9u3h0yVJI6df7/P1ul2G2SfZmuTsJLcluWv66GySA5JcneS7ST6cZEuSo+fIeVXdf3w6yVeTvK/uH36q3s8lSf4lyY1JjqjvM5Hk2iR3Am/qss+tSVbNuL1fksvqfP4hyc/MaPsfJ/m7JP9/kovmeX73SvLeuq3fTLJuxrrX1/HPJPlEkvPq+Evr5+fbSV6+u/1Lkh6W5C1J7kyyLcmv1rE/SrK9Xt6e5NaO+8yajiLJ8+rtjwWuqO/zk/W6H/UVM/u4JEckuSnJt4CzZuxrIsmVdU6bkzyuf63XOLNApGFwDPAt4GDg0cAvAJcALwSeArwwyXPm2cd5wCuBQ4BHAP9Xkn2AS6nePB8PPAn4xRn3ORlYBVwN/Hod+wvgo3UuO4G3zPO4HwJ+F1gNHD4j/ibg3cDjgH8CnjbPfiRpnPXlfb7B454O/DTwJ8Dr69ivAvcCK4ETgOeXUm7ezT4eA7y83v5a4LPAGuDNdZtWAu8B3l5vfy7wD3V8Z4McTwLuBiaAPwB+e8a6l9X7Owr4r0kes5v9PJ2qz3k8Vbv/ACDJjwO/DxwJfAm4uZTyO/WBkQ11W34aODfJRIN8JanVkjwBmASOoOrf3g5QSvnNUsqKenlFKeXI3e2nlPLxevvPA8+t7/NP8zz8RVTfVZ4IHDQj/k7gk6WUQ4Drgd9ZcMMkYNmgE5CoPhhfVEopSb4K/ATw1VLKVwGSfIjqy8Q/dtwvwPQ5vp8DfpPqg/GbSynfTrKaqgB0Vb3NPsBPAR+vb7+1lPLvSb4EPLP+EP1U4Pg6l4uoCka/1S3pJI8GngBcVm//EaoP2dP5/BrVl5g/L6XctpgnRpLGRF/e5xs87vmllB31+/zP17F/o/r884gZP7tzZynlfyf5PvCVOs+9gGcDhwFn13neU2//s8D6UspDSTbW28+plPLxJA8Af8TDxaJpV5ZSvgSQ5DvAo6iKW9328+UkfwS8tc5t+ujxzvpnWf3zUB1fS/UFY/rLyH5URaS7dvtsSFLL1X3Ca4DXURWK+l1cz4zlnwVeUkrZmeQDwGvr+LOBySRvperXbupzThpTjiDSMPjnUsr0F4Buk7oVZr8xTlv5ow1K+Q2qSvkK4MYk/7m+z22llINLKQdTfamYeeWA23fzmLt73GmpHvpHuU9/6KaU8nbgpVRv0JuTPGs3+5Gkcdev9/n5dHuf/zrVqM/bgQ+VUm7d5V675tZtOcDJdf9yMNVopOn4dH/wEPNIcg7wBmAzs0cPzcy/87G77edFVCNXbwT+W8f9bqx/1lCNpprO8y9m9JGHATfMl68ktV2S44CPAd+gGpXabytnLM/VxwQ4qn4/PwQ4Ywny0hiyQKRh0PkBeh/gp5McXY/SOQv4W+B+YEWSH0vyeOAF03eoz/G9B7iA6mjo0cCtwP71ebuP4OHTzQAopcx63FLK94CvAi9Pshfw6vpxuyql/CtwZ5LTUs0x9KIZ+VwDHFRKuQiYohr6L0lt1a/3+YU+LlSnbb2plLKylPK7C27JwzZT9RePAJ4HfKqO3wC8KEmoDhTM5ziqka1TVKfczTRvgWmGY+ucPgn8PzPizwAeAJ5YSnlmKWV6hNB1wMlJfqJ+Df4n1QgiSdLuHQP8PfDXwC91WX9Pkicm2bt+f53PdqoRnSR5bB27Hzgsyd7AK2ZsewPw4rrvecmM+GYenjLj1cDGpo2RZrJApGF0D3Am8D+o5nH4H6WUvy2l3Es1588XqEYC/eWM+7yZai6hu4EdVMPyHwR+hepo6Z11/H3zPPavUlXc76L6AvPWebZ/KXAh1ZeV78yInwf8aZJ7qObJ+PA8+5GkNunJ+/wiH/tK4EP1ZNP/mOTVi9zP71GdlnUn1Xx1L6vjb6EaqfMd4LHd7zrLe6nadkt9nyfVH/wX6mKqLyr/TFVYemyS5VSnGRwN3J3kjnpC7EeVUm6h6uO+CHwNeNc8czFJkiqXUU1b8S9U01nsSH2hgtobqeYV+g7ViNX5nA+ck+RfefjAwgXA+6n6rE/M2PbV9TbfYvY8d68CjklyN/B8qtPfpAXLwyO+JUmSxluSLwKnlVLuTnIUMFVK2d3kzyMtyQbgB6WUP61Hu14DvL2UcsWAU5MkSUPGEUSSJKlNLgO+kuQuqlO73pTkhfXlhTt/zh9wrrMkOXSOPD+3m7ttBl5Wt3crsI3qamySJEmzOIJIkiRJkiSp5RxBJEmSJEmS1HIWiCRJkiRJklpu2aATmLZixYqyatWqBd/vgQceYP/99+99QktgVHM376U3qrm3Pe+bbrppeymlyVWM1CNN+pJR/btswraNrnFun23bM/YlS2+x30tGzTj/b3Zje8eb7d29pn3J0BSIVq1axY033rjg+01NTTE5Odn7hJbAqOZu3ktvVHNve95J7tjzbLQQTfqSUf27bMK2ja5xbp9t2zP2JUtvsd9LRs04/292Y3vHm+3dvaZ9ybynmCU5Kcm2JNfXP0cnuTLJzUkuSWXfJrHG2UuSJEmSJGnJNJ2D6L2llONKKccBTwe2lVKOBg4ETgTOaBiTJEmSJEnSkGl6itkLkjwX+BbwQ+CyOn4tcAJwGHB5g9hVPchZkiRJkiRJPdSkQHQ78OZSyqYkXwCeBnygXnc/cCRwEHBfg9gsSdYD6wEmJiaYmppacAN27NixqPsNg1HN3byX3qjmbt6SJEmSNBqaFIjuBa6pl7cCPwMsr28vB7YDBzSMzVJK2QhsBFizZk1ZzKRSozwZ1ajmbt5Lb1RzN29JkiRJGg1N5iB6PfDCJHsBTwE2AOvqdWuB64DNDWOSJEmSJEkaMk0KRO8GXgp8Gfg41ellK5NsoRpdtBm4tGFMkiRJkiRJQ2beU8xKKXcCkx3hUzpuP9gwJkmSJEmSpCHT9DL3kiRJkiRJGlNNL3M/Nlads2mX2NbzTx5AJpIkSRo33T5rgp83NXidf5sbVu/kLP9eJc3gCCJJkiRJkqSWs0AkSZIkSZLUchaIJEmSJA29JMuS/E2Szyf5YJJ9k1yZ5OYkl6TSKDbotkjSMLJAJEmSJGkU/CJwcynlWOAQ4JXAtlLK0cCBwInAGQ1jkqQOFogkSZIkjYK/A/44yTLg0cBTgavrddcCJwBrG8YkSR1adxUzSZIkSaOnlLIDIMmXgTuBg4D76tX3A0cuIDZLkvXAeoCJiQmmpqb60oZB2rB656zbE/vtGps2ju3fsWPHWLZrLrZ3vPWrvRaIJEmSJA29JAcBO4CfoxoJdDiwvF69HNgOHNAwNkspZSOwEWDNmjVlcnKyL20YpM5L2m9YvZMLb+n+dXDr6ZNLkNHSmpqaYhxf17nY3vHWr/Z6ipkkSZKkUbAB+OVSyn8A3wfOA9bV69YC1wGbG8YkSR0sEEmSJEkaBe8Bzk7yReAe4APAyiRbgHupCkGXNoxJkjp4ipkkSZKkoVdK+TbVCKCZTum4/WDDmCSpgyOIJEmSJEmSWs4CkSRJkiRJUstZIJIkSZIkSWo5C0SSJEmSJEktZ4FIkiRJkiSp5SwQSZIkSZIktZwFIknSkkiyd5JP1stJ8uEkX0ryiSTLkuyb5MokNye5pN5ml9ig2yFJkiSNIwtEkqS+S7IfcBNwYh06FlhWSjkGeBSwDjgD2FZKORo4sN62W0ySJElSj1kgkiT1XSnlB6WUo4Btdegu4KJ6+Yf177XA1fXytcAJc8QkSZIk9diyQScgSWqfUso3AJI8D9gH+DTwGuC+epP7gSOBg7rEZkmyHlgPMDExwdTU1G4fe8eOHfNuM6ps2+ga5/a1rW0bVu/suu24PgeSpPFhgUiSNBBJTqMqCp1aSvmPJNuB5fXq5cB24IAusVlKKRuBjQBr1qwpk5OTu33cqakp5ttmVNm20TXO7Wtb2846Z1PXbbeePtk1LknSsPAUM0nSkktyMPAG4ORSyvfq8GaquYigOrXsujlikiRJknrMApEkaRBeAhwCfDrJ9UnOBi4FVibZAtxLVRzqFpMkSZLUY55iJklaMqWUw+vfFwAXdNnklI7bD3aJSZIkSeoxRxBJkiRJkiS1nAUiSZIkSZKklrNAJEmSJEmS1HIWiCRJkiRJklrOApEkSZIkSVLLeRUzYNU5m7rGt55/8hJnIkmSJEmStPQcQSRJkiRJktRyFogkSZIkSZJazgKRJEmSJElSy1kgkiRJkiRJajkLRJIkSZIkSS3XuECU5HVJrkmyIsnnktyS5Px6XaOYJEmSJEmShk+jAlGSw4Cz6puvBTYBRwPPSXLEAmKSJEmSJEkaMssabncR8NvA64G1wKtKKQ8l+QxwwgJiX5+50yTrgfUAExMTTE1NLbgBO3bsWND9Nqze2XjbxeSzEAvNfViY99Ib1dzNW5Ik9UqSABcDRwLfBTYC7wO21pu8DLgDuAw4FNgCnAk8sjNWSilLmLokjYR5C0RJXgzcDHytDh0E3Fcv3w88ZgGxWUopG6ne2FmzZk2ZnJxccAOmpqZYyP3OOmdT4223nr7wfBZiobkPC/NeeqOau3lLkqQeOhZYVko5JskU8BDw3lLKedMbJPk1YFsp5ZQkVwInAk/oErtq6dOXpOHW5BSzU4BnAR8FngasAJbX65YD2+ufJjFJkiRJWoy7qM5sAPhh/fsFSW5Icnk9wmgtcHW97loePrOhMyZJ6jDvCKJSyosBkqwC3g98AViX5KvAM6nepA9tGJMkSZKkBSulfAMgyfOAfYDbgTeXUjYl+QLVd47OsxiOnCM2Sy+mvhh2nVNtTOw39/Qb49j+tk0hYHvHW7/a23QOopneCXwcOB34ZCnltiSNYr1KWpIkSVL7JDkNeA1wKlWRaGu9aivwOLqfxXBAl9gsvZj6Yth1TrWxYfVOLryl+9fBfk+1MQhtm0LA9o63frW3cYGolLIVeHZ98/iOddubxCRJkiRpMZIcDLwBOKmU8kCS84CvJ7kEeArwB1TFoHXA5VSnlr2Dag6izpgkqUOjy9xLkiRJ0oC9BDgE+HSS64HvAy8Fvgx8vJTyNeBSYGWSLcC9wOY5YpKkDos5xUySJEmSllQp5QLggo7weR3bPEh1kZ2ZusUkSR0cQSRJkiRJktRyFogkSZIkSZJazgKRJEmSJElSy1kgkiRJkiRJajkLRJKkJZFk7ySfrJf3TXJlkpuTXJJKo9ig2yFJkiSNIwtEkqS+S7IfcBNwYh06A9hWSjkaOLCON41JkiRJ6jELRJKkviul/KCUchSwrQ6tBa6ul68FTlhATJIkSVKPLRt0ApKkVjoIuK9evh84cgGxWZKsB9YDTExMMDU1tdsH3rFjx7zbjCrbNrrGuX1ta9uG1Tu7bjuuz4EkaXxYIJIkDcJ2YHm9vLy+fUDD2CyllI3ARoA1a9aUycnJ3T7w1NQU820zqmzb6Brn9rWtbWeds6nrtltPn+walyRpWHiKmSRpEDYD6+rltcB1C4hJkiRJ6jELRJKkQbgUWJlkC3AvVSGoaUySJElSj3mKmSRpyZRSDq9/Pwic0rG6aUySJElSjzmCSJIkSZIkqeUsEEmSJEmSJLWcBSJJkiRJkqSWs0AkSZIkSZLUchaIJEmSJEmSWs4CkSRJkiRJUstZIJIkSZIkSWo5C0SSJEmSJEktZ4FIkiRJkiSp5SwQSZIkSZIktZwFIkmSJEmSpJazQCRJkiRJktRyFogkSZIkSZJazgKRJEmSJElSy1kgkiRJkiRJajkLRJIkSZKGXiofTvKlJJ9IckCSK5PcnOSSev2+TWKDboskDSMLRJIkSZJGwbHAslLKMcCjgLOBbaWUo4EDgROBMxrGJEkdlg06AUmSJA3OqnM2dY1vPf/kJc5EmtddwEX18g+Bc4GX17evBU4ADgMubxC7qv/pStJosUAkSZIkaeiVUr4BkOR5wD7ATcB99er7gSOBgxrGZkmyHlgPMDExwdTUVF/aMEgbVu+cdXtiv11j08ax/Tt27BjLds3F9o63frXXApEkSZKkkZDkNOA1wKnA+4Dl9arlwHbggIaxWUopG4GNAGvWrCmTk5P9acAAndUxWnDD6p1ceEv3r4NbT59cgoyW1tTUFOP4us7F9o63frXXOYgkSZIkDb0kBwNvAE4upXwP2Aysq1evBa5bQEyS1MECkSRJkqRR8BLgEODTSa4H9gZWJtkC3EtVCLq0YUyS1GHeU8ySLAP+CvgJ4FbgvwGXAYcCW4AzgUc2iZVSSu+bIEmSJGnclVIuAC7oCP9Zx+0HgVMaxCRJHZrMQfSLwM2llF9O8inglVSXiTwlyZVUl4l8QsOYVwuQJEkagLmuViZJkgTNTjH7O+CP65FEjwaeClxdr5u+TOTahjFJkiRJkiQNmXlHEJVSdgAk+TJwJ80vHbkkl5Nc6OXd5rqUYzf9vkzeqF6Kz7yX3qjmbt7anST7A38JrAA+D7wN+DjVwYhNpZRzkqzojA0qX0mSJGmcNZmD6CBgB/BzVCOBDmeILie50Mu7dV7ecXf6fXnHUb0Un3kvvVHN3bw1j9OBL5VS/nuSTVTzSGyiKhR9NckHqea0mxUrpXx9YBlLkiRJY6rJHEQbgK+VUj6S5PvAeVSXibyc6jSyd1DNN9QkJknStAeBH0sSYF+qU5j/sJTyUJLP8PDpyq/qiM0qEC10NOo4jxCzbaNrKdq3kFHU0LuR1OP82nVr21zP87g+B5Kk8dGkQPQe4JIkvwHcDnwAuLy+TOTNVJeJ3Ad4foOYJEnT/hL4IvDLVH3E45l9avJj2PV05cd07mSho1HHeYSYbRtdS9G+hYyiht6NpB7n165b2+Z6nvs9Ml2SpD3VZA6ib1MdwZ2pyaUjvZykJGl3fht4Xynl/Un+CjiC2acm30F1enJnTJIkSVKPNbmKmSRJ/fDjwL/Vyw9SjSZal2Qv4JnAdVQjizpjkiRJknrMApEkaVDeA7wiyReB/YDnAb8AbKG6YtltwDu7xCRJkiT1WJM5iCRJ6rlSylbg2I7w8R3bbO+Mabismmu+lfNPXuJMJEmStCccQSRJkiRJktRyYzuCaK4jmpIkSZI0rvweJGmxHEEkSZIkSZLUcmM7gqgXulXfnVNBkiRJkiSNG0cQSZIkSZIktZwFIkmSJEmSpJazQCRJkiRJktRyFogkSZIkSZJazgKRJEmSJElSy1kgkiRJkiRJajkLRJIkSZIkSS23bNAJSJKk8bPqnE27xLaef/IAMpEkSVITFogkSZLGTLcCnSRJ0u54ipkkSZIkSVLLWSCSJEmSJElqOU8xkyRJkiRpiM116rDz+6mXHEEkSZIkSZLUchaIJEmSJI2EJHsn+WS9fFKSbUmur3+OTLJvkiuT3JzkklR2iQ26HZI0jCwQSZIkSRp6SfYDbgJOnBF+bynluPrnVuAMYFsp5WjgwHrbbjFJUgcLRJIkSZKGXinlB6WUo4BtM8IvSHJDksvrkUFrgavrddcCJ8wRkyR1cJJqSZIkSaPoduDNpZRNSb4APBM4CLivXn8/cOQcsVmSrAfWA0xMTDA1NdXfzPtow+qdjbab2G/ubUe5/XPZsWPHSLdroa/VqLd3oWxvb1ggkiRJkjSK7gWuqZe3Ao8DtgPL69jy+vYBXWKzlFI2AhsB1qxZUyYnJ/uVc9+dNcfVrjptWL2TC2/p/nVw6+mTPcxoOExNTTGOr+tcr9Wot3ehbG9veIqZJEmSpFH0euCFSfYCngL8L2AzsK5evxa4bo6YJKmDBSJJkiRJo+jdwEuBLwMfL6V8DbgUWJlkC9UIo81zxCRJHTzFTJI0MEneCJwK7ABeAlwOPBrYVEo5J8kK4OMzYwNLVq21qsuw/q3nnzyATHbVLTdp3JVSDq9/3wlMdqx7EDil4y7dYpKkDo4gkiQNRJInAU8upRwPfAr4E2ATcDTwnCRHAK/tEpMkSZLUYxaIJEmD8izgwCSfBY4HnghcXUp5CPgMMy5N3BGTJEmS1GOeYiZJGpTHAneXUk5L8kXgGcy+DPFj2PXSxI/p3MlCL008zpdBHUTbml5OGfbsssmDfN26tbHXuSy2fQt5/heqV21s2/9cmy4bLkkaLxaIJEmDcj9wa738TarLE8+8DPEd7Hq54js6d7LQSxOP82VQB9G2ppdThj27bPIgX7dubez1JaAX276FPP8L1as2tu1/bqGXopYkaVh4ipkkaVBuAp5eLx9OVSxaV1+u+JnMuDRxR0ySJElSj1kgkiQNRCnli8D2JF+hKg6dCfwCsIXqimW3Ae/sEpMkSZLUY55iJkkamFLKKzpCx3es394ZkyRJktR7FogkSdKSWDXX3Cznn7zEmXQ3V36SJEltMG+BKEmAi4Ejge8CLwY+ChxKNeT/TOCRwGXzxUoppectkCRJGnMWryRJUr81mYPoWGBZKeUY4FHA2cC2UsrRwIHAicAZDWOSJEmSJEkaMk0KRHcBF9XLPwTOBa6ub18LnACsbRiTJEmSJEnSkJn3FLNSyjcAkjwP2IfqssT31avvpzr17KCGsVmSrAfWA0xMTDA1NbXgBuzYsaPr/Tas3rngfTWxmBznMlfuw868l96o5m7ekiRJkjQaGk1SneQ04DXAqcD7gOX1quXAduCAhrFZSikbgY0Aa9asKZOTkwtuwNTUFN3ud1afztXfevquj7VYc+U+7Mx76Y1q7uYtSaOr27xHwzKhuCRJ6r15TzFLcjDwBuDkUsr3gM3Aunr1WuC6BcQkSZIkSZI0ZJrMQfQS4BDg00muB/YGVibZAtxLVQi6tGFMkiRJkiRJQ6bJHEQXABd0hP+s4/aDwCkNYpIkSZIkSRoyjeYgkiRJ6jYnjSRJksZDk1PMJEmSJEmSNMYcQSRJkgZqrpFJXjFLkiRp6TiCSJIkSZIkqeUcQSRJkrRAjnqSJEnjxhFEkiRJkiRJLecIIkmS1CrDcjW2mXlsWL2Ts+rbjkKSJEmDYIFogRxSLkmS5tLtc4KfESRJ0iiwQCRJkoZStxE2FlskSZL6wwKRJElSHy30lLZhOQVOkiS1iwUiSZIkSZJG0FwHFS4+af8lzkTjwAKRJEkaGQudC9DRONJ4SbI38LFSyqlJ9gUuAw4FtgBnAo9sEiullAGkL0lDzcvcS5IkSRp6SfYDbgJOrENnANtKKUcDB9bxpjFJUgcLRJIkSZKGXinlB6WUo4BtdWgtcHW9fC1wwgJikqQOnmImSZIkaRQdBNxXL98PHLmA2CxJ1gPrASYmJpiamupb0v22YfXORttN7Df3tqPc/rns2LFj6Np1y7fv6xpfvXL5LrGmr+u0YWxvP9ne3rBAJEmSJGkUbQemv0kvr28f0DA2SyllI7ARYM2aNWVycrJvSffbWQ3nXtuweicX3tL96+DW0yd7mNFwmJqaYthe17leq27Pf9PXddrFJ+0/dO3tp2F8ffupX+31FDNJ0kAleV2Sa5KsSPK5JLckOb9et0tMkqTaZmBdvbwWuG4BMUlSB0cQSZIGJslhwFnA3cBrgU3A24CvJvkg1dVnZsVKKV8fULqt4ZW/JI2IS4HnJ9kC3ExVCNqnYUyS1MECkSRpkC4Cfht4PdVR3VeVUh5K8hkenli0M2aBSJJarJRyeP37QeCUjtVNY5KkDhaIJEkDkeTFVEdyv1aHOicRfcwcsc79LGhi0XGexLBXbVvoRJhLYXeTqQK869IrusY3rO5XRr01X/uGxWL+vtr2P9emSX8lSePFApEkaVBOAZ4A/DzVFWUeYvYkonew6wSkd3TuZKETi47zJIa9attCJ8JcCrubTHUcjEz7bnmga3jr+SfPeZe2/c8tZNJZSZKGiZNUS5IGopTy4lLKccALgZuA9wDrkuwFPJMZE4t2xCRJkiT1mAUiSdKweCfwC8AWYFMp5bY5YpIkSZJ6bATGMkuSxlkpZSvw7Prm8R3rtnfGJEmSJPWeBSJJkiTtkVVd5t3Z3bxE/dqHJElaPE8xkyRJkiRJajlHEEmSJGkodRtVtDuOOJIkafEsEEmSJElSn8xV6LSgKWnYeIqZJEmSJElSy1kgkiRJkiRJajkLRJIkSZIkSS1ngUiSJEmSJKnlLBBJkiRJkiS1nAUiSZIkSZKklvMy95IkSeq56Ut7b1i9k7NmXObbS3tLkjScLBBJktRSq2Z8aZckSVK7WSCSJEnSkulnYbLbvh2xJElSM40KREn2Bj5WSjk1yb7AZcChwBbgTOCRTWKllNLzFgyJuT7s+KFEkiRJkiQNu3knqU6yH3ATcGIdOgPYVko5GjiwjjeNSZIkSZIkacjMO4KolPID4Kgkt9WhtcDl9fK1wAnAYQ1jV83cd5L1wHqAiYkJpqamFtyA7957H++69Ipd4htWL3hXfbG7Nu3YsWNRbR408156o5q7eUuSJEnSaFjMHEQHAffVy/cDRw6K9YUAACAASURBVC4gNkspZSOwEWDNmjVlcnJywcm869IruPCW4Z1Kaevpk3Oum5qaYjFtHjTzXnqjmrt5S5IkSQvjRSQ0KIuprGwHltfLy+vbBzSMSZKkHuj24XHD6p1MLn0qkqQx4/yqUjstpkC0GVhHdfrYWuAdwBMaxiRJUh/5oV6SJEmLsZgC0aXA85NsAW6mKhjt0zAmSZIkSZKGXLeDTh5wGm+NC0SllMPr3w8Cp3SsbhqTJGksOXJHGk7+b0qS1Mzwzu4sSZJ6xgkvJUmStDt7DToBSZIkSVqMJCcl2Zbk+vrn6CRXJrk5ySWp7NsZG3TekjSMLBBJkiRJGmXvLaUcV0o5Dng6sK2UcjRwIHAicEaXmCSpg6eYSZIkSRplL0jyXOBbwA+By+r4tcAJwGFUV1aeGbtqqZOUpGFngUiSNBD1EP+LgSOB7wIvBj4KHApsAc4EHkn1Qf9HsVJKGUS+kqShdDvw5lLKpiRfAJ4GfKBedz9VH3MQcF9HbJYk64H1ABMTE0xNTfUswQ2rd3aN9/Ixmjxep4n9mm87rV85L4UdO3YMXf4Lff4XYiHtveXb93WNb1i9a2zYnsNpw/j69lO/2muBSJI0KMcCy0opxySZAs6mOgXglCRXUp0C8IQusbE96utE0pK0YPcC19TLW4GfAZbXt5cD24EDusRmKaVsBDYCrFmzpkxOTvYswbPmupLe6b17jCaP12nD6p1ceMvCvg72K+elMDU1RS9f115o+lotxsUn7d+4vQvJY1j/Bobx9e2nfrXXApEkaVDuAi6ql38InAu8vL7d+LSAhR717dcRl14cId7TI4mLORo8Ksa5bTDe7RvWtvXifaDb+8lSjxYRrwe+nuQS4CnABmAdVd+xFngH1cGGzpgkqYMFIknSQJRSvgGQ5HnAPsBN7HoKwLynBSz0qG+/jrj04gjxnh5JXMzR4FExzm2D8W7f0Lbtlge6hreef3LjXXR7P1nq0SLi3cBfAa8EPk51etnlSbYANwObqfqY53fEpD0y16jfhbyH9NMt375vl/ejYclNw2sIe2tJUlskOQ14DXAq8D4WcVrAIHgqmCQNh1LKncBkR/iUjtsPdomNPPsiSb3mZe4lSQOR5GDgDcDJpZTvUR3RXVevXgtcN0dMkiRJUo85gkiSNCgvAQ4BPl1d0IxLgJVtOC3Ao77S8Or2/+lpGZKkNrBAJEkaiFLKBcAFHeE/67g9lqcFSJLGj8V/SaPOAlGfeRRKkiRJkiQNOwtEkiT1kUeUJUmSNAqcpFqSJEmSJKnlHEEkSZIkSdKYc1Sz5uMIIkmSJEmSpJZzBJEkSZK0G3Mddd+weidneURekjQmHEEkSZIkSZLUco4gGoDpo1CdR522nn/yoFKSJEmSJEkt5ggiSZIkSZKklnMEkSRJkiQNgbnmuxqWMw265TcsuQ2LYX8Npd2xQCRJkiRJ0hy8PLzawgKRJEmSJC0xiw6Sho1zEEmSJEmSJLWcI4gkSZIkSVLPOW/VaLFANESc0EySJEmSKhYXpKVlgUiSJEmSNDBtmI9p3Ns47u1rCwtEkiRJkqTWs8ihtrNANAIcWilJ/eUHQkmSpPn5mWm8WSCSJEmSJC3KQgsGw3Cg2yKH1J0FIkmSJEkaYhY01Fb9vJCTF4nalQUiSZIkSZK0JCzMDC8LRCNqFIdySpIkSZI0zNo8B7AFIkmSJEnSkljq0+WmH2/D6p2c5al60m5ZIGqJNldBJUmSJI0HT08aX861NXh9KxAl2Re4DDgU2AKcWUop/Xo8LZxvrpKGnX2JJGlP2ZdI7dGvIlNbvjv3cwTRGcC2UsopSa4ETgSu6uPjqUccbSRpiNiXSJL2lH1JCzj6pF06X29PIeyNfhaI1gKX18vXAifgG/HImvkPOEr/fBa2pJFnXyJJ2lP2JZL6YqkLk/3+fpt+ja5M8mng7aWUa5L8GvD0Usqvd2yzHlhf3zwSuHURD7UC2L5HyQ7OqOZu3ktvVHNve96HlVIe24P9tFaf+pJR/btswraNrnFun23bM/Yle2gJv5eMmnH+3+zG9o4327t7jfqSfo4g2g4sr5eX0yX5UspGYOOePEiSG0spa/ZkH4Myqrmb99Ib1dzNWz3Q875knF9f2za6xrl9tk1DYEm+l4yatv392t7xZnt7Y69e73CGzcC6enktcF0fH0uSNJ7sSyRJe8q+RJIa6GeB6FJgZZItwL1Ub8ySJC2EfYkkaU/Zl0hSA307xayU8iBwSr/2P8MoDwUd1dzNe+mNau7mrT3Sp75knF9f2za6xrl9tk0DtYTfS0ZN2/5+be94s7090LdJqiVJkiRJkjQa+nmKmSRJkiRJkkaABSJJkiRJkqSWG9kCUZJ9k1yZ5OYklyTJoHPqlGTvJJ+sl3fJt2lsCfNNkg8n+VKSTyQ5YNhznpH7siR/k+TzST44Cs93R/6vS3JNkhVJPpfkliTn1+saxZY435OSbEtyff1z9Ig932+sn79PJXncKDzn2nPD8vfXa5mnrxl0fovVpE8adI6L1aTPGnSOe2p3/dqoatL3DTpHaS6d7zsz4q9Lcs0gc+uHbu3t+Py3z6Bz7KUu/cr+Sa6ob79t0Pn1yzj2Nbszo737Jflkkq8k6elcRCNbIALOALaVUo4GDgROHHA+syTZD7iJh/Pqlm/T2FI5FlhWSjkGeBRw9gjkPO0XgZtLKccChwCvHJXckxwGnFXffC2wCTgaeE6SIxYQW2rvLaUcV0o5Dng6o/N8Pwl4cinleOBTwJ8wOs+59szA//56rWFfM6qa9EmjqkmfNbIa9GujbL6+TxpWs953kvx0x//quOls71OZ/fnv8QPNrvc6+5XfAL5U335ykv880Oz6YMz7ml10tPcFwJZSytOBE5P8VK8eZ5QLRGuBq+vla4ETBpjLLkopPyilHAVsq0Pd8m0aWyp3ARfVyz8Ezm2Y3zC8Fn8H/HGSZcCjgad2yWlYc78I+O16eS1wdSnlIeAzzMixQWypvSDJDUkuB57F6DzfzwIOTPJZ4HjgiYzOc649Mwx/fz3VsK8ZVU36pFHVpM8aZfP1a6Nsvr5PGlad7zv3M/t/ddx0tvfpzP7898+DTK4POtu7H/Bj9cjGfan60XEzzn1NNzPb+4/AJfVyT1/bUS4QHQTcVy/fDzxmgLk00S3fprElUUr5RinlhiTPA/ahOio91DlPK6XsKKV8H/g81ZeKoX++AZK8GLgZ+FodGom8gduBN5dSnkF1lOL5DXMcdN4AjwXuLqX8F6qjR8/oktOw5q4904bXcGza2LBPGkkN+6yR1LBfG1VN+j5pKHV53zmG2f+rY2WO99mZn/+OG2R+vdalvX8IPIeqkPBPpZTbB5lfr415X7OLzvaWUm4qpfxTktcCN5VSevZ/PMoFou3A8np5eX17mHXLt2lsySQ5DXgNcCrw3Yb5Dfy1SHJQkkcCP0c1zPspDfMcdO6nUB2B/CjwNGBFwxwHnfe9wPT56luBh7rkM4x5Q9Vp3Fovf5Mq/1HJXXumDa/hWLWxQZ80khr2WaOqSb82qpr0fdJQ6vK+cyEz/leTvHKQ+fVal/Z+j9mf/1YOKrd+6NLeNwHvK6X8JPCYJD830AR7b5z7mm5mtTfJK5O8gmo03Et6+UCjXCDaDKyrl9cC1w0wlya65ds0tiSSHAy8ATi5lPK9Uch5hg3AL5dS/gP4PnBel5yGLvdSyovreQxeSHV0/D3AuiR7Ac+cmWOD2FJ6PfDC+vGfQvX8D/3zXbuJapgxwOFUHxZG4TnXnhuGv79+G5s2NuyTRlWTPmskNezXRlWTvk8aVp3vOy+b+b9aSnn3QLPrvc72PorZn/++OajE+qSzvacA/1avexA4YFCJ9cOY9zW76NLezwEnA79SSvn3Xj7WKBeILgVWJtlCdURn84DzmU+3fJvGlspLqIZMfzrJ9cDeI5DztPcAZyf5InAP8IERyn2mdwK/AGwBNpVSbltAbCm9G3gp8GXg44zQ811K+SKwPclXqIpDZzIaz7n23MD//pbAOLWxSZ80qpr0WeNinN47m/R90rDqfN/59IDz6bfO9p7PjM9/pZQbBppd73W294XAK+rb+zH+70/j1Nc08V+BVcBnUl1V8zm92nFKKb3alyRJkiRJkkbQKI8gkiRJkiRJUg9YIJIkSZIkSWo5C0SSJEmSJEktZ4FIkiRJkiSp5SwQSZIkSZIktZwFIkmSJEmSpJazQCRJkiRJktRyFogkSZIkSZJazgKRJEmSJElSy1kgkiRJkiRJajkLRJIkSZIkSS1ngUiSJEmSJKnlLBBJkiRJkiS1nAUiSZIkSZKklrNAJEmSJEmS1HIWiCRJkiRJklrOApEkSZIkSVLLWSCSJEmSJElqOQtEkiRJkiRJLWeBSJIkSZIkqeUsEEmSJEmSJLWcBSJJkiRJkqSWs0AkSZIkSZLUchaIJEmSJEmSWs4CkSRJkiRJUstZIJIkSZIkSWo5C0SSJEmSJEktZ4FIkiRJkiSp5SwQSZIkSZIktZwFIkmSJEmSpJazQCRJkiRJktRyFogkSZIkSZJazgKRJEmSJElSy1kgkiRJkiRJajkLRJIkSZIkSS1ngUiSJEmSJKnlLBBJkiRJkiS1nAUiSZIkSZKklrNApKGS5Nwk5/Zp38cmuaKH+1uVZGuv9idJkiRJ0qBYIFJrlFI+X0p5bmc8ycVJzhpASpKk3UgylWRyxu03Jnljnx6r9GO/S21c2iFJw2Ap+6F6/z09oC0t1LJBJyBJktREKeVtg85BktRe/e6HSimfB3Y5oC0tFUcQaaglOSPJ1iR3TI/ySTJZV/MvTPKvST6bZL8Z67YmuTXJh5JcNWNfk0mmZtx+WpLtwAuBdyXZnmRdve5HRwtmnkqWZCLJtUnuBN40Y18HJLkkyb8kuTHJEX1+aiRJkiRJ6hkLRBpaSX4SuAB4JnAc8PtJVterjwG+BRwMPBp4Th2/AHgF8Frg8FLKurn2X0q5qZSyAvgo8KpSyopSylVzbV87F/gHYCWwc0b8zXU+K4H3AG9v2ExJGmt1wf2yJHcm+cP69+8meWmS25J8O8nLZ2x/bpK7kmwGlnfsa5d56pK8PMm3knwnyW/VsVX1wYLXJ7k7yZYkBzfI9UV1Pt9M8uQ69qQkn6n385Ek+9fxrUlW1cs/OgCR5D8l+UqSe+oDGI+t4xNJrqzbvznJ4+bJ5bYkh9TLP5PkC/XyEUluSPLdej/L59nPXAc8PLAhqRVGrB+adUC7jm1Ncnad610z3tNXJLmi7m8+P6NPWpHkE/XjbkryuHq//6t+v/9Uko8l2VZvu6D+SePNApGG2YnAJ0spd5RSvgV8DJgu+NwNXFRKeRD4KvCoOv5vVKdOTv/0QmYs/yxwSSnlIWDjjPizgfXAncD5wH/q0WNL0ji4AfhL4Kep3iufBWwA1tSxc+sPqP838FLgp4DfAY7e3U6T7AucRfXefDjwm0l+vF59CFXR/hDg28CLG+R5MrAKuBr49Tr2F1QHEg6mOjDwlnn28SrgWmAF8Angv9Txd1L1aYcA19ft252PASfVy88BLquXzwTeX0p5HHAbcPp8jZqDBzYktcmo9ENzOb3O80+A19exi4AtwGOBTwHn1fE/Ab4GTAB/D7yrjj8OOI2qb7kAuIuqnQvtnzTGnINIo6TwcLHmn0spZUZ82teA/071t/2yHj3uyhnLAR6qlx/qiJ9cSvlyklC9AUuSKl+hGg36FeB7wFHAvsA/1ev3A44EngpsKqXcA9yTZMvudlpK+bckv0r1wfl44EDgoHp1gDeXUnYmuYGHDyTszltLKf+e5EvAM+sP+U8Fji+llCQXURWMfqvjfjMPJHwe+D2qAxlXlVKm2/BsYDLJW4FHADfNk8tlwG8CH6L6MH9GHf994FeSvB84lerARFMz83w2cBhwdh2/ZwH7kaRRMyr90FzOL6XsqPunn69jJwE/VUp5KMl5PDz44znAk+v4HwN3AO8F/rGU8i/VVxVuqJ+HvVh4/6Qx5ggiDbNrgFOSPCHJSuD5wKfrdQ91bpzkEVQflp9RSvnJepK3JrYDT6z38dg6dj9wWF3sefWMbW8AXlTHXzojvhl4eZ3D86iq+JKkSmdBP8BflFIOLqUcTFWouIHZRXjo8l4/U5InAZ+hKsa8Btg2Y/V3Sinf73jc+dw+z/YzD1TM9KMDCaWUv6YaifQ94K/z8FUyAxxVt/cQHi74dH+gUm4AjkiyAlhWSvnf9aorgGcAFwN/Pk975syThw9sHEw1OuqEBe5LkkbJqPRDc5mrf5ruk5bTfYTSzH7rR/edcaB9eh+N+yeNNwtEGlqllH8EzgE+C3wBeEsp5ZbdbP8fVMMo76jnVPhskqMaPNR7gNOS3Av8v3XsIqrh91cBn5ux7VuohqJ+h2o457TfozrycGe9Ta9GL0nSOCrAyUl+Ismjgf9JdeT2BuA5SQ5M8jTmGdpPdaT3W8CHqYbeHzpj3W4/1HdNqjp9eObt71GdxvzyJHtRHTD423r19IGEvanmvgMgyQeBnyul/BnwN1Rz5kF1IGH6tLVXM/s05blcSzX33f83I3Yc1ZHgrcAvNtjHXAc8PLAhqc2Gsh/ajW77ugr4jXr5DOCX6uW/A15Vv++/lvnf3xfTP2lMeYqZhkop5dyO2x8BPtIRmwImZ9w+CyDJGuABqtO7QjUX0EuB13W734z7/zPwMx2xzVTnEU97Vx2/k2p4auc+vsfi54GQpLa5BbgE+CKwN3BhKeVmgCQfBW4Fvk512vDuXEP1Hv8dqg/AtwNH1PftlV+lGq1zHtUo1rfW8QuA9wPfpJpraPpiCe8ALk7yDqovDdN9w6uADya5m6p9TfqMy6gOUsyc1+5tVHNEbKUqXs03ufRFwJ9RfXn4BNXoI6gObLyP6sDGvzB7VKwkjbtR6ofm8hrgA3W/8jWqOeqm4x8Evkt1St1ZVHMNzWUx/ZPGVGaPLpNGV6oruXwEeDpVgeg24NfqkUiSJEmSJGkOFogkSVIrJLkCOLbLqmdNHzle4nzeCLyxy6q3lFL+dKnzkST117D1Q1InC0SSJEmSJEkt5yTVkiRJkiRJLTc0k1SvWLGirFq1qm/7f+CBB9h///37tv9Bs32jb9zb2Mb23XTTTdtLKY+d4y7qg7n6knH/+5tmO8eL7Rwvi22nfcnS69aXDOvf6bDmBea2WOa2OOa2e037kqEpEK1atYobb7yxb/ufmppicnKyb/sfNNs3+sa9jW1sX5I7BpNNe83Vl4z739802zlebOd4WWw77UuWXre+ZFj/Toc1LzC3xTK3xTG33Wval3iKmSRJkiRJUstZIJIkSZIkSWo5C0SSJEmSJEktZ4FIkiRJkiSp5SwQSZIkSZIktZwFIkmSJEmSpJazQCRJkiRJktRy8xaIUvlwki8l+USSU5JsS3J9/XNkkn2TXJnk5iSX1PfZJbYUDZIkSZIkSdLCNBlBdCywrJRyDPAo4CHgvaWU4+qfW4EzgG2llKOBA4ET54hJkiRJ0qIk2TvJJ2fcfmOSzyX5VJJ9kqyob9+S5Px6m11ikqRdLWuwzV3ARfXyD+vfL0jyXOBbwC8Ba4HL63XXAicAh3WJXTVzx0nWA+sBJiYmmJqaWlQjmtixY0df9z9otm943fLt+7rGV69cPuv2KLexCdsnSWqDVeds6hq/+KT9lziT8ZNkP+DLwBH17ScBTy6lHJ/k1cDjgbOBTcDbgK8m+SBwZmeslPL1QbRhVMz1d7z1/JOXOBNJS2neAlEp5RsASZ4H7APcDry5lLIpyReAZwIHAdPfgu8Hjpwj1rnvjcBGgDVr1pTJyck9actuTU1N0c/9D5rtG15nzdXBnj456/Yot7EJ2ydJkvZEKeUHwFFJbqtDzwIOTPJZqoPa76I6cP2qUspDST5DdZC6W8wCkSR1aDKCiCSnAa8BTqUqEm2tV20FHgdsB6aHQyyvbx/QJSZJkiRJvfBY4O5SymlJvggcx64HqR8zR2yW+c5sGNaRwv3Ka8PqnV3jC3msYX3OwNwWy9wWZ5hz6zRvgSjJwcAbgJNKKQ8kOQ/4epJLgKcAf0BVDFpHdUrZWuAdwBO6xCRJLZVkb+BjpZRTk+wLXAYcCmyhGv7/yCaxUkoZQPqSpOFzP3BrvfxNYCW7Hri+Y47YLPOd2TCsI4UXkle308bmOmWs6Qj43fk/7N1/lGRneR/476MIIVnCA5JwwwqQzLJSEhjLhrGP10JRS7bEDwlscJzlSATGbJizxDgGJvaKzZLDbg5ewVnWlgwxTDCLlwicNQqHoLHDD6HG/BD+oRCNckjAgh0FOQ5mzEbyYCx2rHf/qBrTXVM1U11d3XWr6vM5Z87cem7Vreft233vrafe971d/ZklcpuU3CbT5dwGjTNJ9cuTPDHJR6rq00n+PMlPpzf+94OttS8kuTXJBVV1KMk3ktwxIgbAEurPG3F3vnPDgnFvbuCGBwCMcneSH+wvPy29ItEdSa6pqtPSmwrjzhExAAaMMwfRm5O8eSD8poHnPJzkuoHnDIsBsISGzBsx7s0NTnnDAwCWU2vtrqo6UlW/n+Tft9Z+r6q+kuSDSW5I8uHW2n1VdctgbIZpA3TWWHMQAcCUjXtzg1Pe8GCcO2LO09jvrdDOxaKd82nU3C2L1s5Zaq09bd3yqwbWHUly+aliAJxIgQiAWRj35ganvOHBOHfEnKex31uhnYtFO+fTqLlb3vPcsxeqnQAsnnHmIAKAabsjvRsZJL3hZnduIgYAAEyZAhEAszDuzQ3c8AAAAHaAIWYA7Jjj80Zs4uYGbngAAAA7QA8iAAAAgCWnBxEAAMASumjEpOrAclIgAgAA4JSGFZQO33TtDDIBtoMCEQAwdT5EAADMF3MQAQAAACw5BSIAAACAJadABAAAALDkFIgAAAAAlpwCEQAAAMCSUyACAAAAWHIKRAAAAABL7vRZJwAAzK+Lbjw46xQAAJgCPYgAAAAAlpwCEQAAAMCSUyACAAAAWHIKRAAAwFyoqkdV1YcHYq+tqo/3l8+vqk9V1b1VddOoGAAnUiACAAA6r6rOSnJ3kqvXxS5Msnfd016T5GCSS5M8r6ouHhEDYIC7mAEAAJ3XWvtWku+rqvvWhW9O8vokr+s/virJz7bWHqmqTya5ckTsS+u3XVX7kuxLkpWVlaytrW1476NHj54Q64LN5LV/97FtyWHU+//JNx7Mr9z6oRPiuy/YtS15bEZX92cit0nJbToUiAAAgLlTVdcnuSfJF9aFz0vyYH/5oSTnjoht0Fo7kORAkuzZs6etrq5uWL+2tpbBWBdsJq+9Nx7clhwO3zD8/X/l1g/lrfee+HFz1PN3Ulf3ZyK3ScltOhSIAACAeXRdkqckeU6SS6rq1UmOJDneRWVXkvtHxAAYoEAEAHPooiHfBO/ffSyrO58KwEy01q5Pkqq6KMm7Wmtvq6rvSXJNVX0+yRXpDUF78pAYAANMUg0AACyKW5I8P8mhJAdba/eNiAEwQA8iAABgbrTWnjbw+HCSH+svH0ly+cD6E2KLbFgPU4Bx6EEEAAAAsOQUiAAAAACWnCFmAAAAc+aiGw9m/+5j23b7emD56EEEAAAAsOQUiAAAAACWnAIRAAAAwJIzBxEAsMGoWyQfvunaHc4EAICdcsoeRNXz61X1uar6V1V1TlXdXlX3VNV7++vPHCe2Ew0CAAAAYHPGGWJ2WZLTW2s/nOS7k7wiyQOttUuTPC7J1UleOmYMAAAAgI4ZZ4jZ15Lc3F/+dpI3Jnll//EnklyZ5MIkt40R++j6DVfVviT7kmRlZSVra2sTNGE8R48e3dbtz5r2ddf+3ceGxgfbM89tHIf2AQAAdNcpC0SttT9Mkqp6UZIzktyd5MH+6oeSXJLkvDFjg9s+kORAkuzZs6etrq5O2IxTW1tby3Zuf9a0r7v2jprL44bVDY/nuY3j0D5gFHMeAQDM3liTVFfVC5P8XJIXJHlHkl39VbuSHElyzpgxAEiSVNXZSd6X5Pwkn0nyliQfTPLYJAdbazdW1fmDsVnlO++GFWE2W4AZVcgBAGD+jTNJ9ROS/HySa1trf5bkjiTX9FdfleTOTcQA4LgbknyutXZZkqcneWeSg0kuTfK8qro4yWuGxAAAgCkbpwfRy5M8MclH+jcie2+SC6rqUJJ70isEnZHkxWPEAOC4h5N8V/8ul2cmeWaSX2ytPVJVn0xv7rqrkvzsQOxLM8t4RvTcAaCrRp2j9u/e4USALRtnDqI3J3nzQPidA48fTnLdGDEAOO59Se5K8lPpfYnwpGycu+7cnDif3bmDGxnnhgfzPon4qMnuB62cdeIE+CfbxmaeOw2bfb9Rz5/3/Tku7ZxPo36fF62dACyeseYgAoBt8Pok72itvauq3p/k4mycu+7+9OavG4xtMM4ND+Z9EvFRk90P2r/7WP7OiHYO28bgZPmbfb/N2uz7jXr+vO/PcWnnfBr1+/ye5569UO0EYPGccg4iANgmj0nyF/3lh9PrTXRNVZ2W5Iqsm89uIAYAAEyZHkQAzMrbk9xaVT+T5D8meVGSD6Q3efWHW2v3VdUt6d3F7K9iM8uWLTOXEgBAdykQATATrbXDSS4bCF8+8JwjgzEAlldVPSrJv2ytvaB/k4P3JLkkyZ8keXF6n28+kOTJSQ4leVmSRw/GWmtt57MH6DZDzAAAgM6rqrOS3J3k6n7osiSnt9Z+OMl3J7kmyUuTPNBauzTJ4/rPHRYDYIACEQAA0HmttW+11r4vyQP90NeS3Nxf/nb//6uSfKy//IkkV46IATDAEDMAAGDutNb+MEmq6kVJzkjykSQ/l+TB/lMeSm/42XlDYhtU1b4k+5JkZWUla2trG9YfPXr0hNis7d99LCtn9f7volG5deHn2MX9eZzcJiO36VAgAgAA5lJVvTC9otALWmt/WVVH+XvY4gAAIABJREFUkuzqr96V5EiSc4bENmitHUhyIEn27NnTVldXN6xfW1vLYGzW9t54MPt3H8tb7+3mR7pRuR2+YXXnkxnQxf15nNwmI7fpMMQMAACYO1X1hCQ/n+Ta1tqf9cN3pDcXUdIbWnbniBgAAxSIAACAefTyJE9M8pGq+nRVvSLJrUkuqKpDSb6RXnFoWAyAAd3sjwgAADBEa+1p/f/fnOTNQ55y3cDjh4fEABigBxEAAADAklMgAgAAAFhyCkQAAAAAS06BCAAAAGDJKRABAAAALDl3MYMpuujGg7NOAQAAADZNgQgAlpSiNgAAxykQAcACUfQBAGASCkQwI4Mf4vbvPpa9Nx7M4ZuunVFGAN0yrNjlGAkAsD1MUg0AAACw5BSIAAAAAJacAhEAAADAklMgAgAAAFhyCkQAAAAAS85dzABgh426Fb07dAEAMCt6EAEAAAAsOQUiAAAAgCVniBkAsLCGDeczlA8A4ER6EAEAAAAsOQUiAABgLlTVo6rqw/3lM6vq9qq6p6reWz1jxWbdDoAuMsQMAADovKo6K8nvJrm4H3ppkgdaa9dV1e1Jrk7ylDFjH935Fkxu1N0vAaZJDyIAAKDzWmvfaq19X5IH+qGrknysv/yJJFduIgbAAD2IAACAeXRekgf7yw8luWQTsQ2qal+SfUmysrKStbW1DeuPHj16Qmwn7d99bGh85azR62ZtVG6z/DkeN+v9eTJym4zcpkOBCAAAmEdHkuzqL+/qPz5nzNgGrbUDSQ4kyZ49e9rq6uqG9WtraxmM7aS9I4aY7d99LG+9t5sf6Ubmdu83Twjt9N0lZ70/T0Zuk5HbdIw1xGxgMrjnVtUDVfXp/r9LTAYHAADssDuSXNNfvirJnZuIATDglAWi/mRwd6c3mdtxv9pae3b/3xfznQniLk3yuP5zh8UAAACm4dYkF1TVoSTfSK8QNG4MgAGn7I/YWvtWku+rqvvWhX+yqn48yVeT/O30KvG39dcdn/jtwiGxDXcLONVY32map3F/k9C+btjKGPDj47TnoZ2TmJd9OKlFbx90nTv8wPJorT2t///DSa4bWD1uDIABkwxY/XKSN7TWDlbVZ5NckQkngzvVWN9pmqdxf5PQvm4YNT58HMfHaR++YXV6CXXIvOzDSS16+7ZLVf1CkhckOZrk5el9sfDYJAdbazdW1flJPrg+NrNkAQBggU1ym/tvJPl4f/lwku/J8AnihsUAIElSVU9N8vTW2uVJfjvJLyc5mOTSJM+rqouTvGZIDAAAmLJJCkSvS/KSqjotyTOS/LuYDA6AzfvRJI+rqt9JcnmS703ysdbaI0k+md7Q5KuGxAAAgCmbZIjZ25K8P8mrk3ywtfaFqvpykhf3J367J73i0BlDYgBw3OOTfL219sKquivJD2Xj0ORzc+Jw5XMHNzLOfHZdmyNq1Hxlo3Icd36z43OZLbK1tbWh+3Mz7e7S78LJdO33drssWjtH/S4uWjsBWDxjF4jWTQb3x0lWB9aZDA6AzXooyRf7y19Jb8jy+qHJ9+fE4cr3D25knPnsujZH1Kj5ykbNQTbu/GbH5zJbZIdvWB26PzczB9y8zPXWtd/b7bJo7Rz1u/ie5569UO2EaRp1o4HDN127w5nAcptkiBkATMPdSX6wv/y09IpF1/SHMF+RdcOVB2IAAMCUKRABMBOttbuSHKmq30+vOPSyJM9Pcii9O5bdl+SWITEAAGDKFrsfOgCd1lp71UDo8oH1RwZjsFWGMgAAnEgPIgAAAIAlp0AEAAAAsOQUiAAAAACWnAIRAAAAwJJTIAIAAABYcgpEAAAAAEvObe4BoCNG3X4dAAC2mwLRnBr8ELF/97HsvfFgDt907YwyAgAAAOaVAhEAbCO9ggAAmAcKRHPAhwsAAABgO5mkGgAAAGDJKRABAABzqarOrqoPVdVnquotVXV+VX2qqu6tqpv6zzkhBsCJDDEDAADm1Q1JPtda+9+q6mCSdyY5mOQtST5fVe9O8rLBWGvtSzPLmLENm2rDTXlg++hBBAAAzKuHk3xXVVWSM5P8SJKPtdYeSfLJJFcmuWpIDIABehABAGT0TSF8Ww2d9r4kdyX5qSR3JHlSkgf76x5Kcm6S84bENqiqfUn2JcnKykrW1tY2rD969OgJsZ20f/exofGVs0avm7Xtym0a+2HW+/Nk5DYZuU2HAhEAMDcuuvFg9u8+lr3u8An0vD7JO1pr76qq9ye5OMmu/rpdSe5PcmRIbIPW2oEkB5Jkz549bXV1dcP6tbW1DMZ20qhj3v7dx/LWe7v5kW67cjt8w+qWtzHr/XkycpuM3KbDEDMAAGBePSbJX/SXH06vN9E1VXVakiuS3Jlez6LBGAADFIgAAIB59fYkr6qqu5KcleRFSZ6f5FCSg621+5LcMiQGwIBu9kcEAAA4hdba4SSXDYQvH3jOkcEYACfSgwgAAABgySkQAQAAACw5BSIAAACAJadABAAAALDkTFINADAlF9148ITY4ZuunUEmAACbowcRAAAAwJLTgwgAAIC5MKynZqK3JkyDHkQAAAAAS04PIlhA5sAAAABgM/QgAgAAAFhyehABAJzEqPkuAAAWiQIRAABAByhIA7OkQAQdY/4gAAAAdtpYBaKqelSSf9lae0FVnZnkA0menORQkpclefQ4sdZam3oLFohvDAAAAIBZOOUk1VV1VpK7k1zdD700yQOttUuTPK4fHzcGAAAAQMecsgdRa+1bSb6vqu7rh65Kclt/+RNJrkxy4Zixj67fdlXtS7IvSVZWVrK2tjZpO07p6NGj27r9adi/+9jEr105q/f6rrdxUvOw/5Lp7MNhNtv2YduZ9c9vXvbhpBa9fdupql6b5NokL0nywSSPTXKwtXZjVZ0/GJtdpgAAsLgmmYPovCQP9pcfSnLJJmIbtNYOJDmQJHv27Gmrq6sTpDOetbW1bOf2p2HvFoaY7d99LG+99/QcvmF1egl1yDzsv2Q6+3CYze7XYXnM+ndjXvbhpBa9fdulqi5MsjfJ15O8JsnBJG9J8vmqend6Q5Y3xFprX5pRugAAsLAmKRAdSbKrv7yr//icMWNss1HzGJnkGOiom5O8Psnr0uuh+rOttUeq6pPp9TwdFlMgAgCAKZukQHRHkmvSGz52VZJfSvKUMWMAkCSpquuT3JPkC/3QYM/Tc0fEBrdzyuHKsxwCuJWhp5t1sqGqi2Te2jnp796yDF1dtHaO+t1ctHYCsHgmKRDdmuTFVXUovQv7O5KcMWYMAI67Lr0vE56T3jDkR7Kx5+n9ObHX6v2DGxlnuPIshwBuZejpZp1sqOoimbd2TjrEd1mGri5aO0f9zb/nuWcvVDu7pKp+IckLkhxN8vL0vqA2nx3AJo19ddVae1r//4fTu6hfb9wYACRJWmvXJ0lVXZTkXUk+m+Saqvp8kivSG3725CExAEiSVNVTkzy9tXZ5Vf2DJL8c89kBTGR+vn4DYNHdkt43vDck+XBr7b6qOiE2ywQB6JwfTfK4qvqdJF9L8qQkb93sfHanGq68U0MENzt8tstDbnc6t83sny4P+ZTbZOQ2HQpEsCRMYE5XtdYOJ/mx/sPLB9YdGYwBwDqPT/L11toLq+quJD+UCeazO9Vw5Z0aCrnZYcldHnK707ltZjhvl4e2ym0ycpuO02adAAAAwIQeSvLF/vJXkhzOiXdSHnYXZgAGdLPcDAAAcGp3J3ldf/lp6RWLzGe3hPSWh63TgwgAAJhLrbW7khypqt9Przj0siTPT3IovTuW3ZfeHHeDMQAG6EEEAADMrdbaqwZC5rMDmIAeRAAAAABLToEIAAAAYMkpEAEAAAAsOQUiAAAAgCWnQAQAAACw5NzFDABgG11048Gh8cM3XbvDmQAAjKYHEQAAAMCS04MIAGAG9CwCALpEDyIAAACAJadABAAAALDkDDEDAABgadz7Rw9m75Bhvob4suz0IAIAAABYcgpEAAAAAEtOgQgAAABgyZmDCABgiV00ZB6OxFwcALBs9CACAAAAWHIKRAAAAABLToEIAAAAYMkpEAEAAHOtql5bVR+vqvOr6lNVdW9V3dRfd0IMgBOZpHqJmZQSAIB5V1UXJtmb5OtJXpPkYJK3JPl8Vb07ycsGY621L80oXYDOUiDiBMMKR4pGAAB01M1JXp/kdUmuSvKzrbVHquqTSa4cEVMgAhigQAQAUzCqVyYA26eqrk9yT5Iv9EPnJXmwv/xQknNHxAa3sy/JviRZWVnJ2trahvVHjx49IbYd9u8+tqnnr5y1+dfslK7k9iu3fuiE2KjcdmIfn8pO/a5NQm6T6XJugxSIAACWgCImC+q6JE9J8pwklyR5JMmu/rpdSe5PcmRIbIPW2oEkB5Jkz549bXV1dcP6tbW1DMa2w95N/p3u330sb723mx/p5jG3wzes7nwyA3bqd20ScptMl3Mb1M2/WAAAJqYYxLJorV2fJFV1UZJ3Jflskmuq6vNJrkhv+NmTh8QAGOAuZgAAwKK4JcnzkxxKcrC1dt+IGAAD9CACAADmWmvtcJIf6z+8fGDdkcEYACdSIAIAmFOGkgEA06JABHNg1AeAwzddu8OZADALCkEAwHZTIAIAANhhCr9A10xUIKqq56Z3l4DD/dDPJHlTencIOJTkZUkeneQD62OttbbFfAEAFtrxD437dx/b9C2vAQAmtZW7mP1qa+3ZrbVnJ/nBJA+01i5N8rgkVyd56ZAYACRJqufXq+pzVfWvquqcqrq9qu6pqvf21585GJt13gAAsIi2MsTsJ6vqx5N8Ncm30+stlCSfSHJlkguT3DYQ++j6DVTVviT7kmRlZSVra2tbSOfkjh49uq3bn4b9u49N/NqVs07++mFt38z7zfpnNw/7L9nefTjMqJ9JF/ftvOzDSS16+7bJZUlOb639cFWtJXlFel8sXFdVt6f3xcJThsQ+OnKLAADARCYtEH05yRtaawer6rNJnpXk1/rrHkpySZLzkjw4ENugtXYgyYEk2bNnT1tdXZ0wnVNbW1vLdm5/GrbSjXz/7mN5672jd+fhG1a39H7DXr+T5mH/Jdu7D4cZtV+6uG/nZR9OatHbt02+luTm/vK3k7wxySv7j6f6ZcNOFPC2UiCelkkKzfNIO3eGLxAmM2qfLVo7AVg8kxaIvpHk4/3lw0l+IMmu/uNdSY4kOWdIDACSJK21P0ySqnpRkjOS3J0Tv1iYypcNO1HA68JcMZMUmueRdu4MXyBMZtSx4D3PPXuh2gmLyJ2DWXaTXnW8LsmXquq9SZ6RZH+Sa9L7lveqJL+U3rCAwRgA/JWqemGSn0vygiTviC8boDOGfVDyIQkAFtekk1S/LclPJ/ndJB9Mb3jZBVV1KL3eRXckuXVIDACSJFX1hCQ/n+Ta1tqfpXeeuKa/+qokd46IAQAAUzZRD6LW2h8nWR0IXzfw+OEhMQA47uVJnpjkI/2bk7033/li4Z70ikNnJHnxQAwAAJiyxR/AD0AntdbenOTNA+F3Djz2ZQMAMFOG3LIsJh1iBgAAAMCCUCACAAAAWHKGmMEcG3UrTgAAANgMBSIAAMYy6osJc3EAwPxTIGKuuDAFAACA6VMgYizLUJhxdwIAmIxzKADMP5NUAwAAACw5PYiWhMmMAQBYNFVVSd6T5JIkf5Lk+iS/keTJSQ4leVmSRyf5wPpYa63NIl+ALlMgYukolgHA7Ayeh/fvPpa9Nx40JI1JXZbk9NbaD1fVWpJXJHmgtXZdVd2e5OokTxkS++jMMgboKAUiFsIyzJEEAMAJvpbk5v7yt5O8Mckr+48/keTKJBcmuW0gpkAEMECBaAb0YAEAgK1rrf1hklTVi5KckeTuJA/2Vz+U3tCz84bENqiqfUn2JcnKykrW1tY2rD969OgJsa3av/vYlrexctZ0trMdFj23af8+HLcdv2vTIrfJdDm3QQpEAADA3KqqFyb5uSQvSPKOJLv6q3YlOZLknCGxDVprB5IcSJI9e/a01dXVDevX1tYyGNuqvVP40nj/7mN5673d/Ei36LkdvmF1OskM2I7ftWmR22S6nNugbv7FAgAw1/SYZidU1ROS/HyS57bWvllVdyS5Jr0hZVcl+aX05iAajAEwQIEIJuCiFwCgE16e5IlJPtK7oVnem+SCqjqU5J4kd6Q39OzFAzEABigQAQCwsNzIYrG11t6c5M0D4XcOPH44yXU7kxHLwrGFRaRAxI5xEAUARhl2neAagUWg5zkwLxSIAADoJF8uAYtCEZx5oEDElrhwAwAA6NFjjHmmQMS2cGAEAHaa6w9gngw7Zu3ffSyrO58KJElOm3UCAAAAAMyWHkQAAMwVPYUAYPr0IAIAAABYcnoQAQAAQEe44xmzokBEZ60/MO7ffSx7Z9CdXBd2AFhM7sQKABspEAHAJikeAwCwaBSItpkPEbPl5w8AAMw7vR7ZCQpEsOSMcQaA79jMl0vOlwAsEgUiZk4vHwBgHrmGAWCRKBABAADAHDL0jGlSIAIAAABOSUFqZ8zq56xABIxtOw9U5kICAOaZIYfAvJv7AtG4B+L9u49l740HfeAEAAAAGDD3BaKdpksdnEjvHwAA6I5pXJ8PbmP/7mMZVUJwB8jFoEA0JbqUskj8PkOPvwUAYFF05bpGp4vu2rYCUVWdmeQDSZ6c5FCSl7XW2na937g2U0ntyh8QLCtzHrFT5xLHe4DF1dXPJcCpKSbtrO3sQfTSJA+01q6rqtuTXJ3ko9v4fhPzwQCmb1iX1L3+1ti8uTmXANBZziUwBzbzubwrnzUWrVBV21U8r6r3JbmttXZbVb0uyeNba68feM6+JPv6Dy9J8sVtSabn/CRHtnH7s6Z982/R27iM7buwtfb4WSSzKKZ4Lln037/jtHOxaOdimbSdziVbNKVzSVd/T7uaVyK3ScltMnI7ubHOJdvZg+i8JA/2lx9K70C7QWvtQJID25jDX6mqP2it7dmJ95oF7Zt/i95G7WNCUzmXLMv+0c7Fop2LZVna2VFbPpd0df91Na9EbpOS22TkNh2nbeO2jyTZ1V/eldlXzACYP84lAGyVcwnAGLazQHRHkmv6y1cluXMb3wuAxeRcAsBWOZcAjGE7C0S3Jrmgqg4l+UZ6B+ZZ2pGhbDOkffNv0duofUxiWueSZdk/2rlYtHOxLEs7u2ga55Ku7r+u5pXIbVJym4zcpmDbJqkGAAAAYD5sZw8iAAAAAOaAAhEAAADAklvIAlFVPaqqPrzucVXVr1fV56rqX1XV6evWvbaqPj6bTCczTvtO1uZ5MGYbz6yq26vqnqp6b1XVLHPejMH2DYtV1dlV9aGq+kxVvWXns5zcOO3rx36hqj5VVb9dVWfsbJaTG7d9/fjcHWMWxTwfI5KNv1PD2jLt2AzaN3hcP2fR2thv5+lV9Zv9Y/m7F3FfDrT3tVX18ao6v3rH93ur6qb+uqnGZtS+51bVA1X16f6/Sxd5fy67Lu+P/u/Lr1eHr/Wro9dA1dHrz+rotX+d4nqkQ7l16m+ihn9e6OTfxHoLVyCqqrOS3J3k6nXhy5Kc3lr74STfnf5dDKrqwiR7dzrHrdhE+4a2eR5soo0vTfJAa+3SJI8beH5nDWvfiDbfkORzrbXLkjy9qv7GjiY6oXHbV1VPTfL01trlSX47yZN2ONWJbGL/zeUxZsHM5TEiGfo7Nawt047ttMHj+iu2kHtX25gkP5Hknv6x/IlJXj1mrvPWzsFj3muSHExyaZLnVdXF2xCblV9trT27tfbsJD+YBd2fJOn2/uj0tX5Xr4E6fv3ZuWv/Ma9HupJbZ/4mRnxe6OTfxKCFKxC11r7VWvu+JA+sC38tyc395W+vi9+c5PU7lds0bKJ9o9rceZto41VJPtZf/kSSK3cmw60Z1r4RbX44yXf1K/NnZk724yba96NJHldVv5Pk8iT/z85mOplNtC+Zw2PMgpnLY0Qy9HdqWFumHdtpg8f1Nw7Jad7bmCT/Osn/0f8m87FJnjkkr0VoZ7LxmHdVko+11h5J8smsy3WKsVn5yar6vaq6Lb1z2aLuT7q9P7p+rd/Va6AuX3927tp/zOuRmRiSW2f+JkZ8Nujq38QGc18gqqp/uq6b76er6hcHn9Na+8PW2u9V1YuSnJHkI1V1fZJ7knxhp3PejEnbNyy207mPa9I2JjkvyYP9pzyU5Nydy3p847RvhPcleV6Sf5/kP7TWvrx9WU5uC+17fJKvt9b+Vnrf3jx7+7Kc3KTtm5djzIKbi2PEmIa1ZdqxHTXkuH73mHnOTRuTpLV2tLX250k+k97F68Lty2ToMW8h25nky0ne0Fr7ofR6hL14SF6L0E56Ors/unyt3/FroC5ff87Dtb+/iQl0/G9ig86NVd2s1trfH+d5VfXCJD+X5AWttb+squuSPCXJc5JcUlWvbq29bRtTncik7RsV66It7MMjSXb1V+9KcmSbUtyScds3xOuTvKO19q6qen9V/Uhr7bPTzG0attC+h5J8sb/8lSQXTCej6dpC++biGLPg5uIYMaZhbTlnyrEdt/64nuQdQ3JahDael+Rokh9J79vWpw3Ja+7bmYFjXpJHBvK6Pyf+Hm81NgvfSHJ8/ojDSX4gi7k/6en0eaTD1/pdvgbq8vXnPFz7+5uYTJf/JjaY+x5E46iqJyT5+STXttb+LElaa9f3x46/JMndXd1B4xjWvmGxeTaiPXfkO2NLr0py5yxy20aPSfIX/eWH07toXCR3pzd3Q9L7wPSVGeYydYt0jJlji3SMGNaWacd21JDj+sK1sW9/kp/qX6j+eZI3Dclr7ts5eMxL8vYk11TVaUmuWJ/rFGOz8LokL+nn8Yz09u/C7U/+Smf3R5ev9Tt+DdTl6895uPb3NzGBjv9NbLAUBaIkL0+vG/BH+kNEXjHrhKZsWPsWrc3D2nNrkguq6lB63+jdMcsEt8Hbk7yqqu5KclYWrH2ttbuSHKmq30/yxdba7806JxbOIh0jhrVl2rGdtuG4nuRRW8i9q21MesfyV/SP5X+a5NfGzHXe2jnoliTPT3IoycHW2n3bEJuFtyX56SS/m+SDWZ79uay6vD8W7Vp/R3T8+nMerv39TSy4aq3NOgcAAAAAZmhZehABAAAAMIICEQAAAMCSUyACAAAAWHIKRAAAAABLToEIAAAAYMkpEAEAAAAsOQUiAAAAgCWnQAQAAACw5BSIAAAAAJacAhEAAADAklMgAgAAAFhyCkQAAAAAS06BCAAAAGDJKRABAAAALDkFIgAAAIAlp0AEAAAAsOQUiAAAAACWnAIRAAAAwJJTIAIAAABYcgpEAAAAAEtOgQgAAABgySkQAQAAACw5BSIAAACAJadABAAAALDkFIgAAAAAlpwCEQAAAMCSUyACAAAAWHIKRAAAAABLToEIAAAAYMkpEAEAAAAsOQUiAAAAgCWnQAQAAACw5BSIAAAAAJacAhEAAADAklMgAgAAAFhyCkQAAAAAS06BCAAAAGDJKRABAAAALDkFIhZeVe2tqvdMYTuXVdWHppASAAAAdIoCEQyoqsNVddFgvLX2mdbaj+98RgBMU1WtVtXaGM+7qKoOb+F93lhVb5z09QDMt3HPN9AVCkQAQOdUVdvEc4cW9gHgVJxv4DsUiJip/vCvT1fVv62q/1xV+/rxl/YPwPdX1d5TPHdDZb6q3nP8NSd531dW1Vf72/kf+7F/UFVHkjw5yb+pqiNVdfa615zwDUBVPbWqPllVX6+qf378+f3cX1FV91XV16pqdcs/LAAAANgmCkR0we4kP5Hkh5L8YlVdnuTNSa5I8uwk/6Sqdo947pM3+2ZVdWaSvUn+2yRPS/IPq+oxrbVbWmvnJ/lqkme21s5vrX3zFJv7v5L8RpInJDmW5B+vW3dDku9P8stJXrfZPAGWUVX97/1iffqF+i/2l6+pqi9W1R9V1f/Uj40s7FfVC9cV6X9lCyk9qqpuq6o/qapfrarqb/+Hqurf9L9oOLAu/oL+FxD/Lskz17XrjVX1v1bVW6rqT6vq0f34M9dt55er6q+Nive38dH+9m+pqn9XVR9Z975f7m/7nx3PB4Dhuna+cZ6gCxSI6II7WmuHW2v/Mcnnkqwm+XBr7f7W2leT/Msk14x47rOGbO+kB7vW2l8k+bvpFXB+I8njkpy32aSr6jHpXfy/o7X2l0luTvL8dU+5qbV2tJ/nd292+wDLqLX2D/vF+vQL9ZdU1XlJ3pvkJUmekeQlVfW8UxT2/4ck/12SJyW5sqr+5oQp/VdJ/u8kFybZk+RvV9UZSW5N78uGJyV5apKfqKqzkrw7yU+mdy77/oFt/fdJHkzy11trD1fVo5J8IMn/nOSiJE9Psm9UvL+NY0n+fv9ncU2Sq/vxNyV5dZInJvlrSf7rCdsLsBQ6eL5JnCeYMQUiumB9Qee0JA8NrG/rnjP43EeGbO+Ck75Z1VOTfDLJ15P8XJIHNpPsSazPM0m+vC4OwOR+JMnnW2ufb639v0n+z2wsyA/ziiSXJXlfehfB3zPhe/+X1tq/aK19K8lv9nO5JL0L8o+mdw55ZpK/2Y9/vbX2e621I+l9wbHeva21N7XWvt5//NeTfLu19lv9Ly/+ab9do+JJ8vtJ/izJv2+t/ad857zzqST/MMnLk7yhtXbfhO0FWGazPN8kzhPMmAIRXfBj1ZvL56L0ho79QZLrquopVXVBkhcn+ciI596dXkHpydXz/ekNTTuZZ6ZX/f/19L7dHRymdiTJ91bVaf1vEYZqrf1Zks8neWVVnZbkHyT5rXVPGVa8AmDrBgvyG1TVrvQukFuS/yXJ727hvR4ZWK7+v/taa09orT0hvW+Nf6UfH3z+ep87xXuNatf6eBv4v/egtZ9J8o+SnJ/kD6rqb5zivQA4tZ083yTOE8yYAhFd8Lkk7+//f2Nr7TNJbkzyO0k+m+Qft9buHfHcP0qvSHMovQPy65IeXLugAAAgAElEQVR88BTv9/H0Dpj/OcmPp9fT5+J16/9xkvck+dN8Z2jbKH83yUuTfC3JGemdGADYuj+tqu/td6P/bJLvr6pLq+qx6Q3tWl+QHyzs/zfpHecPJHlsekPDJnVuVf1E9eave3F655ovJjm7ejcv+Gv5znCzLyV5YlX9QFWdm96ceSfzH5KcUVXP6W//Vf12jYqP1J8740/Tm8PvPyS5dLLmAiydrpxvhnGeYEedPusEIMkftdauXh9orf3zJP98zOe2JC8atfHW2nvSK/gcf/xf0usGOur5v5XeXBOD8bX05pRYH/tKkr815LkXnex1AJzSLyT5TJJHp1doeVl6cwGdk+Tt/WP1cccL+9+d3rwLH0jyb5P8pyR3JbknvS8C1ibI44+TvDLJP0vyoST/orX2SFX9nSTvSG+Ooo+kNx/dt6vqlUk+nOSb6RWMRmqt/X9V9beT/Fp/O7+R5J2ttb8cFk/yhpNs7g1JPpbkMel9wLl9grYCLKOunG9O4DzBTqveZ2uYjerdjn61tbZ3ms8FAAAAxqdABAAstar6UIb3LP3R1to9O50PAIvJ+YauUyACAAAAWHImqQYAAABYcp2ZpPr8889vF1100aZf981vfjNnn3329BOaki7n1+Xckm7n1+Xckm7n1+Xckunmd/fddx9prT1+Khubc1X13CTvSnK4H/qZJG9K8uT07kL4svQmh/zAqWLtJF1fF/VcMki+22eeck3ku926kK9zyc5blHOJfE5OPqN1KZdEPqcyTj5jn0taa53496xnPatN4s4775zodTuly/l1ObfWup1fl3Nrrdv5dTm31qabX5I/aB04vnbhX5LnJvlH6x7/vfTu+pT07qJxzbixk73Pop5LBsl3+8xTrq3Jd7t1IV/nkp3/tyjnEvmcnHxG61IurcnnVMbJZ9xzSWd6EAGw8H6yqn48yVeTfDu9nkFJ8okkVya5MMltY8Q+ulMJAwDAslAgAmAnfDnJG1prB6vqs0meleTX+useSnJJkvOSPDhGbIOq2pdkX5KsrKxkbW1t08kdPXp0otfNiny3zzzlmsh3u81bvgCwFQpEAOyEbyT5eH/5cJIfSLKr/3hXkiNJzhkztkFr7UCSA0myZ8+etrq6uunk1tbWMsnrZkW+22eeck3ku93mLV8A2Ap3MQNgJ7wuyUuq6rQkz0iyP705hpLkqiR3JrljzBgAADBlCkQA7IS3JfnpJL+b5IPpDS+7oKoOpde76I4kt44ZAwAApswQMwC2XWvtj5OsDoSvG3j88JgxAABgyvQgAgAAAFhyCkQAAAAAS06BCAAAAGDJLewcRBfdeHBo/PBN1+5wJgAsmmHnGOcXADbDuQToGj2IAAAAAJacAhEAAADAklMgAgAAAFhyCkQAAAAAS06BCAAAAGDJKRABAAAALDkFIgAAAIAlp0AEAAAAsOQUiAAAAACWnAIRAAAAwJJTIAIAAABYcgpEAAAAAEtOgQgAAABgySkQAQAAACw5BSIAAACAJadABAAAALDkFIgAAAAAlpwCEQAAAMCSUyACAAAAWHIKRAAAAABL7pQFoqo6u6o+VFWfqaq3VNX5VfWpqrq3qm7qP2esGAAAAADdM04PohuSfK61dlmSpyd5Z5KDSS5N8ryqujjJa8aMAQAAbFpVPbeqHqiqT/f/XVpVt1fVPVX13uo5c5zYrNsC0EWnj/Gch5N8V/9AemaSZyb5xdbaI1X1ySRXJrkqyc+OEfvS+g1X1b4k+5JkZWUla2trm27A0aNHh75u/+5jQ58/yXtsxaj8uqDLuSXdzq/LuSXdzq/LuSXdzw8AltyvttbelCRV9feSPNBau66qbk9ydZKnjBn76IzyB+iscQpE70tyV5KfSnJHkiclebC/7qEk5yY5b8zYBq21A0kOJMmePXva6urqphuwtraWYa/be+PBoc8/fMPm32MrRuXXBV3OLel2fl3OLel2fl3OLel+fgCw5H6yqn48yVeTfDvJB/rxT6T3hfSFSW4bI6ZABDBgnALR65O8o7X2rqp6f5KLk+zqr9uV5P4kR8aMAQAATOLLSd7QWjtYVZ9N8qwkv9Zf91CSS3Lil9SjYhts58iGUYaNeJhmL+au9YqWz8l1KZ8u5ZLI51Smmc84BaLHJPmL/vLD6fUmuqaqPp/kiiQ3J3nymDEAAIBJfCPJx/vLh5P8QDZ+IX0kyTljxjbYzpENowwb8TDN0Q5d6xUtn5PrUj5dyiWRz6lMM59xJql+e5JXVdVdSc5K8qIkz09yKMnB1tp9SW4ZMwYAADCJ1yV5SVWdluQZSfYnuaa/7qokd6Y3JcY4MQAGnLIHUWvtcJLLBsKXDzznyDgxAACACb0tyfuTvDrJB9MbXnZbVR1Kck96haAzkrx4jBgAA8YZYgYAADBTrbU/TrI6EL5u4PHDY8YAGDDOEDMAAAAAFpgeRAAwBRcNmWw0SQ7fdO0OZwIAAJunBxEAAADAklMgAgAAAFhyCkQA7Jiqem1Vfbyqzq+qT1XVvVV1U3/dWDEAAGD6FIgA2BFVdWGSvf2Hr0lyMMmlSZ5XVRdvIgYAAEyZAhEAO+XmJK/vL1+V5GOttUeSfDLJlZuIAQAAU+YuZgBsu6q6Psk9Sb7QD52X5MH+8kNJzt1EbHDb+5LsS5KVlZWsra1tOr+jR49u6nX7dx8b+7mT5HMqm8131uYp33nKNZHvdpu3fAFgKxSIANgJ1yV5SpLnJLkkySNJdvXX7Upyf5IjY8Y2aK0dSHIgSfbs2dNWV1c3ndza2lo287q9I25pP8zhGzafz6lsNt9Zm6d85ynXRL7bbd7ypZsu2sQ5A2CWDDEDYNu11q5vrT07yUuS3J3k7UmuqarTklyR5M4kd4wZAwAApkyBCIBZuCXJ85McSnKwtXbfJmIAAMCUGWIGwI5prR1O8mP9h5cPrDsyTgwAAJg+PYgAAAAAlpwCEQAAAMCSUyACAAAAWHIKRAAAAABLToEIAAAAYMkpEAEAAAAsOQUiAAAAgCWnQAQAAACw5BSIAAAAAJacAhEAAADAklMgAgAAAFhyCkQAAAAAS06BCAAAAGDJKRABAAAALDkFIgAAAIAlp0AEAAAAsOQUiAAAAACWnAIRAAAAwJJTIAIAAOZGVb22qj5eVedX1aeq6t6quqm/bqwYACdSIAIAAOZCVV2YZG//4WuSHExyaZLnVdXFm4gBMECBCAAAmBc3J3l9f/mqJB9rrT2S5JNJrtxEDIABp886AQAAgFOpquuT3JPkC/3QeUke7C8/lOTcTcQGt70vyb4kWVlZydra2qbzO3r06NDX7d99bOxtTPK+m81nVuRzcl3Kp0u5JPI5lWnmo0AEAADMg+uSPCXJc5JckuSRJLv663YluT/JkTFjG7TWDiQ5kCR79uxpq6urm05ubW0tw16398aDY2/j8A2bf9/N5jMr8jm5LuXTpVwS+ZzKNPMxxAwAAOi81tr1rbVnJ3lJkruTvD3JNVV1WpIrktyZ5I4xYwAMUCACAADm0S1Jnp/kUJKDrbX7NhEDYIAhZgAAwNxorR1O8mP9h5cPrDsyTgyAE+lBBAAAALDkxioQVdUvVNWnquq3q+p7+sv3VtVN/fXnjxMDAAAAoHtOWSCqqqcmeXpr7fIkv53kl5McTHJpkudV1cVJXjNmDAAAAICOGacH0Y8meVxV/U56Y3e/N8nHWmuPJPlkkiuTXDVmDAAAAICOGWeS6scn+Xpr7YVVdVeSH0ryYH/dQ0nOTXLemLENqmpfkn1JsrKykrW1tU034OjRo0Nft3/3saHPn+Q9tmJUfl3Q5dySbufX5dySbufX5dyS7ucHAACwHcYpED2U5Iv95a8k+Z4ku/qPdyW5P8mRMWMbtNYOJDmQJHv27Gmrq6ubbsDa2lqGvW7vjQeHPv/wDZt/j60YlV8XdDm3pNv5dTm3pNv5dTm3pPv5AQAAbIdxhpjdneQH+8tPS69YdE1VnZbkiiR3JrljzBgAAAAAHXPKAlFr7a4kR6rq99MrDr0syfOTHEpysLV2X5JbxowBAAAA0DHjDDFLa+1VA6HLB9YfGScGAADAcBeNmibjpmt3OBNgGY0zxAwAAACABaZABAAAALDkFIgA2HZVdXpV/WZVfaaq3l1VZ1bV7VV1T1W9t3rGis26LQAAsIgUiADYCT+R5J7W2mVJnpjk1UkeaK1dmuRxSa5O8tIxYwAAwJSNNUk1AGzRv07yW1V1epLHJnlmktv66z6R5MokF44Z++j6DVfVviT7kmRlZSVra2ubTu7o0aObet3+3cfGfu4k+ZzKZvOdtXnKd55yTeS73eYtXwDYCgUiALZda+1oklTV7yb54yTnJXmwv/qhJJdsIja47QNJDiTJnj172urq6qbzW1tby2Zet3fEXWaGOXzD5vM5lc3mO2vzlO885ZrId7vNW74AsBWGmAGw7arqvKp6dJIfSW+o2DOS7Oqv3pXkSP/fODEAAGDKFIgA2An7k/xUa+0vk/x5kjcluaa/7qokdya5Y8wYAAAwZQpEAOyEtyd5RVXdleRPk/xakguq6lCSb6RXCLp1zBgAADBl5iACYNu11v4ovR5A61038PjhMWMAAMCU6UEEAAAAsOQUiAAAAACWnAIRAAAAwJJTIAIAAABYciapBoARLrrx4KxTAACAHaEHEQAAAMCSUyACAAAAWHIKRAAAAABLToEIAAAAYMmZpBoAttGwia4P33TtDDIBAIDR9CACAAAAWHIKRAAAAABLToEIAADovKo6vap+s6o+U1Xvrqozq+r2qrqnqv7/9u43VLLzvg/492dW/6jIRpGatVBtqcVYLxpZJd0GE9n1alOpSbUycUrAWMJeBN2+CXXSxa1CW3BfhEov3FY1JvFCS1VVUKgVsKttYsmSbmKHOEn1YlclYOyWLawoTreClRcbGcVPX9yz5e5o7u6ZvfPnmZ3PB4Y988zeme88M/ec8/zuec55praNalv1ewHokQIRAACwDn4xyanW2n1Jbk/yK0nOttbuTXJLkgeSPDqyDYAJTlINAACsg99N8l+ral+SH0/y00meGx57Ocn9Se4c2fbCzieuqmNJjiXJgQMHsrW1NXO4CxcuTP254/e8PfNzTZpnnlWR5/J6ytNTlkSeK5lnHgUiAACge621C0lSVX+U5H8nuTXJ+eHhN5PcPUPb5HOfSHIiSQ4ePNgOHTo0c76tra1M+7mjU65mOaszj8wvz6rIc3k95ekpSyLPlcwzjylmAABA96rq1qq6IcnPZnuq2E8l2T88vD/JueE2pg2ACQpEAADAOjie5Jdba3+e5PtJfiPJg8Njh5O8kuSlkW0ATFAgAgAA1sEXkjxWVX+Y5P8m+bdJ7qiq00neyHYh6NmRbQBMcA4iAACge62117N9BNBORybuvzWyDYAJjiACAAAA2HAKRAAAAAAbToEIAAAAYMMpEAEAAABsuI07SfVdj598R9uZJx5aQRIAAACAPjiCCAAAAGDDKRABAAAAbDgFIgAAAIANt/bnIHrt9fM5OuW8QgAAAACM4wgiAAAAgA03ukBUVb9WVV+rqtuq6utV9VpVPTE8NqoNAAAAgP6MKhBV1Z1Jjg53fzXJyST3JvmFqnr/DG0AAAAAdGbsEURPJfn1Yflwkhdbaz9K8ntJ7p+hDQAAAIDOXPEk1VX1iSSnkvzp0HRrkvPD8ptJfmKGtsnnPpbkWJIcOHAgW1tbM7+BAzclx+95e+af2+lqXnesCxcuLPT596LnbEnf+XrOlvSdr+dsSf/5AAAAFmHMVcyOJHlvkr+d5O4kP0qyf3hsf5L/leTcyLZLtNZOJDmRJAcPHmyHDh2a+Q18/tkv53Ov7e1ibGcemf11x9ra2srVvK9l6Dlb0ne+nrMlfefrOVvSfz4AAIBFuOIUs9baJ1prH0ry8SSvJvlCkger6l1JPpLklSQvjWwDAAAAoDNXc5n7f5Pk7yQ5neRka+07M7QBsIFq29NV9c2q+kpV3VxVz1fVqap6Znj8xjFtq34vAABwLRo9N6u1dibJ3xrufnjisXNj2gDYWPcl2dda+2BVbSV5LMnZ1tqRqno+yQPZns48pu2F1bwFAAC4dl3NEUQAMKvvZvuKmEnywySfTfLicP/l7Lj65Yg2AABgzvZ2dmcAGKG19u0kqaqPJbk+2+e023mly7vzzqtf7tZ2iXlcEXO3q9ft9SqZu9nrlfLW7Wp765R3nbIm8i7auuUFgL1QIAJgKarqo0k+neThJL+VS690eS7JzSPbLjGPK2LudvW6o4+fnPm5xtjr1TPX7Wp765R3nbIm8i7auuUFgL0wxQyAhauqdyf5TJKHWmvfy3Cly+Hhw9lx9csRbQAAwJwpEAGwDJ9KcnuSr1bVN5Jcl+SOqjqd5I1sF4KeHdkGAADMmSlmACxca+3JJE9ONH9x4v5bSY6MaAMAAObMEUQAAAAAG06BCAAAAGDDKRABAAAAbDgFIgAAAIANp0AEAAAAsOEUiAAAAAA2nAIRAADQvdr2dFV9s6q+UlU3V9XzVXWqqp4ZHr9xTNuq3wtAj/atOgAAAMAI9yXZ11r7YFVtJXksydnW2pGqej7JA0neO7LthdW8hatz1+Mn39F25omHVpAEuJYpEAHAkk3b0U/s7ANcwXeTPDUs/zDJZ5P8veH+y0nuT3JnkudGtF1SIKqqY0mOJcmBAweytbU1c7gLFy5M/bnj97w983ONcaWMu+VZFXkur6c8PWVJ5LmSeeZRIAIAALrXWvt2klTVx5Jcn+TVJOeHh99McneSW0e2TT73iSQnkuTgwYPt0KFDM+fb2trKtJ87ussfBfbqzCPvfK0xeVZFnsvrKU9PWRJ5rmSeeZyDCAAAWAtV9dEkn07ycJI/S7J/eGh/knPDbUwbABMUiAAAgO5V1buTfCbJQ6217yV5KcmDw8OHk7wyQxsAExSIAACAdfCpJLcn+WpVfSPJdUnuqKrTSd7IdiHo2ZFtAExwDiIAAKB7rbUnkzw50fzFiftvJTkyog2ACY4gAgAAANhwCkQAAAAAG06BCAAAAGDDKRABAAAAbDgFIgAAAIANp0AEAAAAsOEUiAAAAAA2nAIRAAAAwIZTIAIAAADYcApEAAAAABtOgQgAAABgwykQAQAAAGw4BSIAAACADadABAAAALDh9q06AAAAwLp77fXzOfr4yVXHALhqjiACAAAA2HAKRAAAAAAbToEIAAAAYMMpEAEAAABsOAUiAAAAgA13xQJRbXu6qr5ZVV+pqpur6vmqOlVVzwyP3zimbRlvCAAAAIDZjDmC6L4k+1prH0zyY0keS3K2tXZvkluSPJDk0ZFtAAAAAHRmTIHou0meGpZ/mOSzSV4c7r+c5P4kh0e2AQAAANCZfVf6D621bydJVX0syfVJXk1yfnj4zSR3J7l1ZNslqupYkmNJcuDAgWxtbc38Bg7clBy/5+2Zf26nq3ndsS5cuLDQ59+LnrMlfefrOVvSd76esyX951tnVXVdkt9urT1cVTcm+VKS9yQ5neSTSW4Y09ZaayuIvxR3PX5yavuZJx5achIAADbNFQtESVJVH03y6SQPJ/mtJPuHh/YnOZfk5pFtl2itnUhyIkkOHjzYDh06NPMb+PyzX87nXhv1NnZ15pHZX3esra2tXM37WoaesyV95+s5W9J3vp6zJf3nW1dVdVOSP0ry/qHp4jTkI1X1fLanIb93ZNsLy38HAABwbRtzkup3J/lMkodaa99L8lKSB4eHDyd5ZYY2ADZQa+0HrbUPJDk7NI2dmmy6MgAALMGYQ28+leT2JF8dLkT2TJI7qup0klPZLgRdn+SXRrQBQDJ+avJSpivvNrVwr1OY52Uy27pNhVynvOuUNZF30dYtLwDsxZhzED2Z5MmJ5i9O3H8ryZERbQCQbE877ma68m5TC4/uck6gZZucCr1uUyHXKe86ZU3kXbR1ywsAe7G3k/cAwNW5OA35uWxPI/tX2T7f0Jg2ANh4LmwAzJsCEQCr8GzGTU1eynTl114/383RQgAAsAoKRAAsTWvtfcO/Y6cmm64MwP9XVdcl+e3W2sNVdWOSLyV5T5LTST6Z5IYxba21toL4AF274lXMAAAAVq2qbkryapIHhqZHk5xtrd2b5JahfWwbABMUiAAAgO611n7QWvtAkrND0+EkLw7LLye5f4Y2ACaYYhYneAMAgDV0a5Lzw/KbSe6eoe0SVXUsybEkOXDgQLa2tmYOc+Cm5Pg9b8/8c/N2MfuFCxeu6n0sijyX11OenrIk8lzJPPMoEAEAAOvoXJL9w/L+4f7NI9su0Vo7keREkhw8eLAdOnRo5jCff/bL+dxrqx9enXnkUJLtQtHVvI9FkefyesrTU5ZEniuZZx5TzAAAgHX0UpIHh+XDSV6ZoQ2ACQpEAADAOno2yR1VdTrJG9kuBI1tA2DC6o+BBAAAGKm19r7h37eSHJl4eGwbABMcQQQAAACw4RSIAAAAADacAhEAAADAhlMgAgAAANhwCkQAAAAAG85VzACgc3c9fvKS+8fveTuHVhMFAIBrlCOIAAAAADacAhEAAADAhlMgAgAAANhwCkQAAAAAG85JqgEAAK4RFy9scPyet3N0x0UOzjzx0KoiAWvCEUQAAAAAG06BCAAAAGDDmWIGAGvorh3TBnYyhQAAgKvhCCIAAACADadABAAAALDhTDG7jGmH7zt0HwAAALjWKBABAABc4/zxG7gSBSIAuIY4eTUAAFfDOYgAAAAANpwCEQAAAMCGUyACAAAA2HDOQQQAG8DJSQGY5Lx1wE6OIAIAAADYcApEAAAAABvOFLMZOQwTAAAAuNYoEAHAhvJHDwAALlIgAgAuoXAEsNlsB2AzKRDNyW4r0X//839hyUkAAADmzxUx4dqmQAQAjGJgAABw7VpYgaiqbkzypSTvSXI6ySdba21RrwfAtce2pH+7HUG70/F73s7Rx08qJgErYVuyWGO2A4ltAayDRR5B9GiSs621I1X1fJIHkrywwNfr0muvn8/RkStNK0uAd7AtuYaMHURcNG276LwYwFWwLemIo1GhX4ssEB1O8tyw/HKS+2NFfFmz7jjvlfMjAWvAtmSDzbJdnNc29OJfuHdSqIK1Z1vSuWWPg5Lp6/tFsn1gHSyyQHRrkvPD8ptJ7p78D1V1LMmx4e6FqvrWVbzObUnOXVXCJfgHHee7/8l+sw16ztdztqTvfD1nS+ab7845Pc8msy2ZoudtyzTrlHda1npy/M/P8n/nZG36diDv7GxL9m4jtyW9rXs3Pc+I7UNP/dNTlkSeKxmTZ9S2ZJEFonNJ9g/L+zMlcGvtRJITe3mRqvpvrbWDe3mOReo5X8/Zkr7z9Zwt6Ttfz9mS/vNtINuSKeRdnHXKmsi7aOuWl11t5LZEnsuTZ3c9ZUnkuZJ55nnXPJ5kFy8leXBYPpzklQW+FgDXJtsSAPbKtgRghEUWiJ5NckdVnU7yRrZXzAAwC9sSAPbKtgRghIVNMWutvZXkyKKef4c9HQq6BD3n6zlb0ne+nrMlfefrOVvSf76NYluyK3kXZ52yJvIu2rrlZYoN3pbIc3ny7K6nLIk8VzK3PNVam9dzAQAAALCGFjnFDAAAAIA1oEAEAAAAsOHWtkBUVTdW1fNVdaqqnqmqWvLr/3xVna2qbwy3eyfzTMu4jNxVdV1V/ZdheVSGZWWdyDbZh3evKtvwnE9X1Ter6itVdXNn/TaZ70hHfbevqv5zVf1BVf27veRYUN9N5uvme8fq9fqZ1pzW40vIWTWndeeisw5557a+WkbeIfOvVdXXquq2qvp6Vb1WVU8Mj41qW1LObveLLpP5Hw199TtV9ZM99y99W/b3uKfft+ps3FEdjTWG53m6OhhfTMmy0rFEdTZ+mJKni/FCzWEfYFrb5axtgSjJo0nOttbuTXJLkgdWkOE3W2sfaq19KMnfmJJnWsaF5q6qm5K8uuN5x2ZYeNYp2ZIdfdha+9aqsiW5L8m+1toHk/xYksf2kGMRn/Fkvh+ln777xSSnWmv3Jbk9ya/sIcci+m4y319LP33H6nX3mc55Pb5o81x3LsM811cLV1V3Jjk63P3VJCeT3JvkF6rq/TO0LUt3+0W7qaq/kuSvttY+nOR3kvzr9N+/9GsV3+OV/771Nu7ocKzR0/iit7FEb+OH7sYLc9wHmGm7tc4FosNJXhyWX05y/woy/N2q+uOqei7Jz03JMy3jQnO31n7QWvtAkrND09gMC886JVuyow+H6upKsiX5bpKnhuUfJvnsHnIs4jOezJf003e/m+RfVtW+JD+e5Kf3kGMRfTeZ783003esXnef6ZzX44s2z3XnMsxzfbUMTyX59WH5cJIXW2s/SvJ7O3ONaFuW7vaLLuPnktxSVb+f5MNJ/nL671/6tYrv8cp/33obd3Q41uhpfNHbWKK38UOP44V57QPMtN1a5wLRrUnOD8tvJvmJJb/+/0jyz1prP5PtKuMvTckzLeOyc4/NsIqsk334kVVla619u7X2x1X1sSTXZ/uvD93025R8PfXdhdba95P8QbY3Pl1956bkezGd9B1dWIfPtNvv55zXnQs35/XVQlXVJ5KcSvKnQ1O3WQfrsl900V9M8n9aa38zyV9K8jOd56Vvy/5e9Pr71tU+YFa8v9zT+KK3sURv44fexgtz3geYKc86F4jOJdk/LO8f7i/TG0m+NiyfyfZhepN5pmVcdu6xGVaRdbIPf3KV2arqo0k+neThJH+2hxwL6beJfOfSSd9V1a1VdUOSn832YZQ/tYcci/hcJ/N9IJ30HV1Yh8+06+/nHNedy8g6z/XVoh3J9lEB/ynJX09yW8dZk/XZL7rozSTfGpb/Z7Yz95yXvi37e9Hr71tv446VjzV6Gl/0NJbobfzQ4XhhnvsAM+VZ5wLRS0keHJYPJ3llya//D5N8vKrele0v9PEpeY7DsrgAAAGbSURBVKZlXHbusRlWkXWyD//7qrJV1buTfCbJQ6217+0xx9z7bUq+bvou29/9X26t/XmS7yf5jT3kWMR3bjLfP00/fcfqrcNn2u33c87rzmWY5/pqoVprnxjOLfLxbP/V+QtJHhzWXR/ZmWtE2zKsy37RRa9m+7wtSfK+bBeLeu5f+rbs73Gvv29d7T9nxfvLPY0vOhxL9DZ+6Gq8MOd9gNm2W621tbwluSHJ80lOJ3kmSS359W9PspXkT5L882l5xrYtKN93duunVWfdke2SPtxr3j1m+sdJvpPkG8Pt7/fUb1Py/ZOO+u6ObM+v/cMk/7G379yUfHf00nduq7/1/JlmDuvxJWSc27pzSX06t/XVEr8Hd2X7r5i3Jfl6tndS/8Xw2Ki2JeXser9ol8y/OeT9D733r1vft2V/j3v7fUtn4450MtZIR+OLKVlWOpZIZ+OHKXm6GC9kDvsA09oud6vhhwAAAADYUOs8xQwAAACAOVAgAgAAANhwCkQAAAAAG06BCAAAAGDDKRABAAAAbDgFIgAAAIAN9/8AudX+fWYwc14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize = (20,15)) # 绘制9张柱状图 每张图50等分\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### housing_median_age（房龄） 和 median_house_value（房价） 被设定了上限值\n",
    "   1. 设置了上限的区域，重新收集标签值\n",
    "   2. 把设置了上限的区域数据移除\n",
    "\n",
    "### 重尾 头高尾长\n",
    "   * 转换数据，把数据形状变成偏钟形分布（正态分布， 平均值为0，标准差为1）\n",
    "   * 收入特征\n",
    "   * 数据提供的上游证实 是年薪 单位万美元， 提前对特征进行缩放是正常的，需要得知数据是如何缩放的\n",
    "   \n",
    "### 收入特征\n",
    "   * 数据提供的上游证实 是年薪 单位万美元， 提前对特征进行缩放是正常的，需要得知数据是如何缩放的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   * 训练集用于模型训练 占整个数据集的80% ，测试占20% (打乱后取)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# 测试集 data:所有数据 test_radio:测试集占用比例\n",
    "def split_train_test(data,test_radio):\n",
    "    #设置随机种子，返回相同数据集\n",
    "    np.random.seed(42)\n",
    "    # 对原来的数据进行重新洗牌 生成随机序列\n",
    "    indices = np.random.permutation(len(data))\n",
    "    # 测试集包含的所有样本数 数据长度*所占比例\n",
    "    test_set_size = int(len(data)*test_radio)\n",
    "    # 测试集索引，从从数据开头到测试集样本数\n",
    "    test_indices = indices[:test_set_size]\n",
    "    # 训练集索引，测试集所余下的就是训练集\n",
    "    train_indices = indices[test_set_size:]\n",
    "    # 返回测试集和训练集的索引\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得训练集和测试集（二八分）\n",
    "train_set,test_set = split_train_test(housing,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一的标识符，对标识符取hash值，去hash的最后一个字节，值小于等于51，256*20%，放入测试集\n",
    "# hash值相同，对象不一定相同，hash不同，对象一定不同 \n",
    "import hashlib\n",
    "# 定义测试集标识符\n",
    "def test_set_check(identifier,test_radio,hash = hashlib.md5):\n",
    "    # 把标识符定义成整形，返回摘要，作为二进制的字符串对象\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data,test_radio,id_column):\n",
    "    # 得到样本标识符\n",
    "    ids = data[id_column]\n",
    "    # 用test_set_check方法来验证是否正在测试集 用到了pandas的apply方法\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_,test_radio))\n",
    "    # 返回训练集（不在测试集——取反）和测试集\n",
    "    return data.loc[-in_test_set],data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用行索引作为标识符ID\n",
    "housing_with_id = housing.reset_index()\n",
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(housing_with_id,0.2,'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题：基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引会变\n",
    "### 办法：寻找稳定特征来创建唯一标识符（经纬度唯一）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一列ID\n",
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude'] # 取出经纬度，保证得到不重复的样本ID值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(housing_with_id,0.2,'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调用sklearn，和基础方法相同\n",
    "from sklearn.model_selection import train_test_split\n",
    "# housing,test_size=0.2 占比,random_state = 42 随机种子\n",
    "train_set,test_set = train_test_split(housing,test_size=0.2,random_state = 42) \n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数据可视化中探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分层采样 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽样偏差：调查公司给1000个人来调研几个问题，不会在电话簿中随机查找1000人，他们视图确保1000人代表全体人口，美国人，51.3女性，48.7男性\n",
    "# 你的调查应该维持这一比例，513名女性，487男性，这就是分层采样，人口划分为均匀的子集，每个子集称为一层，然后从每层中抽取正确的实例数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117886748>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 希望测试集能够代表整个数据集中各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 创建一个收入类别属性\n",
    "   2. 不应该数据分层太多，但每一层都应该有足够的数据量\n",
    "   3. 将收入中位数处以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的列别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#新建类别\n",
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "# 把大于5的值改成5\n",
    "housing['income_cat'].where(housing['income_cat']<5,5.0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把连续值转换成类别标签\n",
    "housing['income_cat'] = pd.cut(housing['median_income'],bins = [0.,1.5,3.0,4.5,6.,np.inf],labels = [1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     4\n",
       "4     3\n",
       "5     3\n",
       "6     3\n",
       "7     3\n",
       "8     2\n",
       "9     3\n",
       "10    3\n",
       "11    3\n",
       "12    3\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    2\n",
       "17    2\n",
       "18    2\n",
       "19    2\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计12345有多少数量\n",
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1174db4a8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 收入类别直方图\n",
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据收入类别进行分层采样\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把训练集和测试集分成多少组 n_splits 默认为10\n",
    "split = StratifiedShuffleSplit(n_splits=1,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 遍历split中income_cat的数据\n",
    "for train_index,test_index in split.split(housing,housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>-120.97</td>\n",
       "      <td>37.66</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>3.5395</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.04</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2233.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>8.3839</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>-117.24</td>\n",
       "      <td>34.15</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>140200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.99</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>1916.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1.8242</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>-121.31</td>\n",
       "      <td>38.02</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2.7981</td>\n",
       "      <td>92100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19684</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.14</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>61500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>-122.69</td>\n",
       "      <td>38.51</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.6854</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>34.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2.7120</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>36.91</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.9141</td>\n",
       "      <td>123900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11176</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>33.83</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>3.3831</td>\n",
       "      <td>197400.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15614</th>\n",
       "      <td>-122.41</td>\n",
       "      <td>37.81</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>3.6728</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2953</th>\n",
       "      <td>-119.02</td>\n",
       "      <td>35.35</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.9830</td>\n",
       "      <td>63300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13209</th>\n",
       "      <td>-117.72</td>\n",
       "      <td>34.05</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>4.0614</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>34.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>184200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "19480    -120.97     37.66                24.0       2930.0           588.0   \n",
       "8879     -118.50     34.04                52.0       2233.0           317.0   \n",
       "13685    -117.24     34.15                26.0       2041.0           293.0   \n",
       "4937     -118.26     33.99                47.0       1865.0           465.0   \n",
       "4861     -118.28     34.02                29.0        515.0           229.0   \n",
       "16365    -121.31     38.02                24.0       4157.0           951.0   \n",
       "19684    -121.62     39.14                41.0       2183.0           559.0   \n",
       "19234    -122.69     38.51                18.0       3364.0           501.0   \n",
       "13956    -117.06     34.17                21.0       2520.0           582.0   \n",
       "2390     -119.46     36.91                12.0       2980.0           495.0   \n",
       "11176    -117.96     33.83                30.0       2838.0           649.0   \n",
       "15614    -122.41     37.81                25.0       1178.0           545.0   \n",
       "2953     -119.02     35.35                42.0       1239.0           251.0   \n",
       "13209    -117.72     34.05                 8.0       1841.0           409.0   \n",
       "6569     -118.15     34.20                46.0       1505.0           261.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "19480      1448.0       570.0         3.5395            127900.0   \n",
       "8879        769.0       277.0         8.3839            500001.0   \n",
       "13685       936.0       375.0         6.0000            140200.0   \n",
       "4937       1916.0       438.0         1.8242             95000.0   \n",
       "4861       2690.0       217.0         0.4999            500001.0   \n",
       "16365      2734.0       879.0         2.7981             92100.0   \n",
       "19684      1202.0       506.0         1.6902             61500.0   \n",
       "19234      1442.0       506.0         6.6854            313000.0   \n",
       "13956       416.0       151.0         2.7120             89000.0   \n",
       "2390       1184.0       429.0         3.9141            123900.0   \n",
       "11176      1758.0       593.0         3.3831            197400.0   \n",
       "15614       592.0       441.0         3.6728            500001.0   \n",
       "2953        776.0       272.0         1.9830             63300.0   \n",
       "13209      1243.0       394.0         4.0614            107000.0   \n",
       "6569        857.0       269.0         4.5000            184200.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  \n",
       "19480          INLAND          3  \n",
       "8879        <1H OCEAN          5  \n",
       "13685          INLAND          4  \n",
       "4937        <1H OCEAN          2  \n",
       "4861        <1H OCEAN          1  \n",
       "16365          INLAND          2  \n",
       "19684          INLAND          2  \n",
       "19234       <1H OCEAN          5  \n",
       "13956          INLAND          2  \n",
       "2390           INLAND          3  \n",
       "11176       <1H OCEAN          3  \n",
       "15614        NEAR BAY          3  \n",
       "2953           INLAND          2  \n",
       "13209          INLAND          3  \n",
       "6569           INLAND          3  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看所有住房数据根据收入类别比例分布，收入占类别百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set)  # 分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)  # 完整数据集合\n",
    "# 数据相似，说明测试集获取成功，差不多覆盖了整个数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"全部数据\": income_cat_proportions(housing),\n",
    "    \"分层抽样\": income_cat_proportions(strat_test_set),\n",
    "    \"随机抽样\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100 * compare_props[\"随机抽样\"] / compare_props[\"全部数据\"] - 100\n",
    "compare_props[\"分层. %error\"] = 100 * compare_props[\"分层抽样\"] / compare_props[\"全部数据\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>全部数据</th>\n",
       "      <th>分层抽样</th>\n",
       "      <th>随机抽样</th>\n",
       "      <th>随机. %error</th>\n",
       "      <th>分层. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       全部数据      分层抽样      随机抽样  随机. %error  分层. %error\n",
       "1  0.039826  0.039729  0.040213    0.973236   -0.243309\n",
       "2  0.318847  0.318798  0.324370    1.732260   -0.015195\n",
       "3  0.350581  0.350533  0.358527    2.266446   -0.013820\n",
       "4  0.176308  0.176357  0.167393   -5.056334    0.027480\n",
       "5  0.114438  0.114583  0.109496   -4.318374    0.127011"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props # 随机采样的没有分层采样的精确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把测试集已经处理完成，用分层抽样的训练集数据来进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将地理数据可视化\n",
    "   * 建立一个各个区域的分布图，以便于数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117554400>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter',x='longitude',y='latitude',color = 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11745e320>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAECCAYAAAASDQdFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9aXBc53UmfN5e0Qsa+74DJCiKFAVRpCTKomQtluRVthI5GdsVO4lHk3EmX2aSqWSSH5maTDKZmkqq4sSTcXmSTOIt9jgeeZVlWZIlihJJiZREkaK4ACAAYmkAjbW70Xvf78fDk3PRvN3oBhukQLxPFQpodN9733tJPOe8z9mUYRikoaGhobE1YLveC9DQ0NDQuHbQpK+hoaGxhaBJX0NDQ2MLQZO+hoaGxhaCJn0NDQ2NLQTH9V5ALurr643u7u7rvQwNDQ2NTYUTJ06EDMNoWOtz7znS7+7upuPHj1/vZWhoaGhsKiilRov5nJZ3NDQ0NLYQNOlraGhobCFo0tfQ0NDYQtCkr6GhobGFoElfQ0NDYwthQ0hfKfUflFLPKaU8SqkfKqVeV0p9ZSOuVQyyWaJUCt83CzbjmjU0NN77KHvKplKqi4g+R0SzRPQLRPS2YRgfVUpdVErdbBjGmXJfsxCiUaJLl/Cz00nU2krk8VzLFZSOWIxocpIokyGy2zfHmjU0NDYHNsLT/yIR/cHln98loq9d/jmZ7wCl1JNKqeNKqeOzs7NlW0g0SnTsGNHUFNH8PFE6DTK18p7L4VmX6xyTk0QOB5Hfj+/51ryea+odhIbG1kZZPX2l1KeI6CQRnSEiMgzjxOXf/3siOpHPyzcM4ytE9BUion379l1Vg/9sFh6yUvDwlSKqrCRKJIhmZ4nq6/G+zWTuyuFZl8s7z2Twxcc6nVh7KoU12+2y9lKvuRl3EPzvab5vDQ2N9aPc8s5HiKiTiB4hoh1KqX9HRBkiOkhEv1zma12BWIxofJwomQTZr6wQRSJEExNEFRVE8Ti8Z7tdjjF71h4PyHVykqinpziSYc95fJzI5VrfOcxQCudMJIjcbpwrmSQaGyMyDCFrt7u0dZdyn+sl2nIT9HqMlDYSGhqFUVbSNwzjU0RESqluIvpbInqZiP6UiB4zDCNTzmvlIpslGhqCjKMUvPojR0D4mQxReztRf/9qwifK71nn7gaswKQUj+N7by+OL+UcVudLJokuXgSxV1fjfC4XvjNZt7eXtu5i73O9u4Fy7yLWY4w3405GQ+NaY6N9od8gom4iekkpdVgp9cGNulAqRRQMikf/7LNEhw8TjY6C3CYnxVPOmMyP2bPm89jtVxqHXJhJqaoKBD0+Lp7/WufI1dbN5wuHiV54geirXyX6xjeIjh/HPWWzIGtev92OcxSzbn6v0OdLjSdc7XGFwEbK6cRrvu9MHtdhI9agoXEjYkMarhmGMUJED23EuQtfF6Rw5gzR8jKIoq4O77nd8P7TaSE6s2c9OQm93+eDh7iWh57rObe1wTtfWoLhKXQOK4/U4ZBYxE9+ArJqboaH/9xzeO+mm4gaGvBZzkSanITBUoqoqSn/em221Z/n65rXuN5dz9XslvLBbKR4h1PIqG3EGjQ0bkS857psrhdMgpcuIWvHZiPyeuGFz88L0bW34z2zZ1hfL8Heri78bi3Y7RI3qKjAMV1dRJ2dWEs+osknW3R14ZxLS4hDJJPw7pUC8afTMBYTE0R33onzezyQOyIRoulp7HQKyRr8+Xyad6lEe7XHrYWGBtxXPiN1LdagoXGj4YYhfZsNmrrNBuLfsQMkFwphB9DfT/Tgg9DIicQz5GApE4VRZO5QIoGvYBDHtLbi+m534ePyeaR8jpUVGK10Gq+np7Guzk6cP5WCETBnKc3OXqn559O+bbb8xFnMbuBqjysm0GreCSmFHY/fX3gd6127hsZWww1D+kQg0h07iGproYPX1oL0+/qIbrkFxM8kwF7h0JB4/vX1xXmG7K1XVsKIxOMg7bUIn69r3iEwAdrtIO3+fqJPfILopZdA+OEwCFwpxAzq67ELCAZxbDaL1/X1OP96ZA0zEa+1G8iHYo4rJtBqtROanQXpl2MNGhpbHTcU6RPhD72pieiRR0QeYQ08lwQMA+8T4XuxXn6ut+71QmIphmh5h8Dk195OtG2bHOd0wnD19YEkR0Zwbp8PhBiPS3qowyEFZ5WVsmspRdbIR8TrIcxCu4his3GuVpsvtAYNDY0bkPQZnMWRD5kMiLO3VwhvZaU4clmvfszE53TCUMRi8OZ7e+UzZpnC6cQ97N6Ndc3OwsNPpXC8UiB6NjqJhMQ2iq0xuJoahVJQLJlfjTavc/Q1NNbGDUv6VjCTAn9xWmAp5LJe/TiTwXXm54X4l5YQgzBLTyxTsH5vtxPNzOD9ykqid9+Ft9/QAGnH5yM6eBDHNjcXn5t+LTNeiiVz87ONxfC7jo7iayZ0jr6GRmFsGX8oFkNKJX8lEiCGdFqyZRpyRgoX6lPDxMxfxRAMExx75Om0xAFy889tNrzX3o7Ph8P4fW0t0lFZsopEYBAMA6+DwdW5/4X67BSTu18s1roWkzk/bw5UW5G5xwPjxQgGxQDku7bO0dfQKA5bwtMvJGPkS3kkWttzLFY/Nu8wOjpw3qUlkHptLcg6H9F6PND8ifB59sSbmhBEHhuDEfvxj6XquLNzddyg0PrLkfFSrJddbKA1m8W/hcdTXEaSztHX0CgeW+JPYq3qTk55ZC9xfBxf5fAcc3cYNhvy7FtahPDXIlquAVhZwTkyGXj7IyOQhurqUI8wN0d09izeL9bzXc+OxYxSvWybrXAdA1Hp1bjl3LFoaNzo2BKkX4gUrAgmmcRXsaSTD/kI0eOBR97XZ020VlKJ2w3DtH070WOPEdXUSF+hpSVUIQ8NQeZhL7fY9RdDxPlQKkEXg1JJvBTpSENjq2NLyDtMCuPjIAWXSypzia4MMHLx08oKfuaunaV6joVkh3wkm08qyWRA6D4f8vv7+0Fup04hIOxy4WtmRuSiclan5suMKSZAW2pWzXpkJ52jr6FRHLYE6RcKMloRTF0dBq8MDqK4q76eqLsb75cif5Saflgo9pDr/Waz0O6DQQR5l5ag/be34/1yVqcW0uytnl9zs3j6xcQWrLAeEtc5+hoaa+OGJ/1oFJ02x8ZAvJ2d8IjNgUEzwRgG0fAwvOmqKqRIch8f7pHD3TrL2Z6AaO2dAZ8rlUKRls2GYG51NQxVZyeMARdulcPzZUNks+Gc/NocVDU/P3O1sFJYf2Xl+uoANIlraJQfNzTpR6PoqR+JIMBZVYXA5513ivfPKZM2GwhqdBRfHg8IrLZWGriFw/D+eYIVe62F5ItSPNa1dgbm/H3O9+/vJzp3DkYgnSbau1caxvG12Ovm16XILZkM7p8rju12xCdyM2P452BQdiorK3jN/Y6uNqtGF19paFw9bljSz2aJLlwA6YyMEB09CmL0eoneeYforrtATp2d0r5gfBw/V1bi+Pl5ePx2Owg1FALp8ucnJyFlsGe73tROM5mttTPgc7ndCARnMkS7dhEtLkqTOYaVLEO0ttySTsPguVzw1kMhPAefD88wFJL2FWbk7lQqKrAr4ud+NbEFXXyloVEe3LCkn0oh954IzddYn89kUNHKxUzLy/BEuad+by9y4KenQVrRKNIrUyl4/dyczekEEV26BPJZbxsDKzJba2dgzjyy20GqPt/qhm9W8YHxcbxXaKxjMIhh8nY7iHrXLsQ0IhE8C7sdr636FOXuVDIZ3I9h4Pj1xhauZbsIDY0bHTcs6WezErh94w0hViLINC+/LG0NHnsMhD43B2Ls65M8+t5eeLXxONHrrxMtLIBceTgL0ep0RbN8sZYcUYjM+JxWx2Qy2GGMjODz3H7YHGi2ig9EIvjZ57Ne7+go0Ze/LFlCt9yC7KCODoltpNNYp5Wnb9VCobtbso/WK8vo4isNjfLhhvyT4QHp8TjRj36En81545kMPP9kEgYgFMLv29pAaEtLINeurtW9b3ggeSwGgmxpEe2dCESUzYIQc4uyzG0E0mno3bEY1shyx1o57uZzcgFUTw8malVWri6Kssp157ROzmRaWZFU1GQSIya5atjtBuHH4/Dss1k8l9FRfHZ01Lo1glULBW47UY4pWnwvuvhKQ2N9uOE8ffaeo1GiL34RfemtkEyC7HftAglnMvmnXzERBwIgGh6xODVF1NgI739pSdI7ubePVdbK4iJ2HsvL+AoEsGtobQUBW9UDcNCZWyp7PPDax8eh4zOp8jpZ98+ND3A65/DwlTuEZBIevt+P1z4fYhqZDALgVVUwlLkxjVyJpdQWCsVAD0jR0CgfbjjSz2TggX7nO/kJnwiE7PeDNG02HON0ghjNw8c5U8duB+HMzuJ9Hoa+sADPeHhYCDFf1koiAb18YUGCodksCPLVV7FzaG1dLdOw5h+Pw8hw9s7YGNGbb+K4jg7o79u3r27TbJU5xCmdPT1SeDY+LudwubD2+Xms7847YQxTKQkgm+8pV2LZKClGF19paJQHNxzpJ5PwtF94If9nqqqIDhwAyXV2Eu3cKTJOIiH9bcxZIq2tkDTCYRiMpiYQIJf9mwkxX9YKk3U2i/O4XDi+vR3n52Eq7BkTieZfVQVDMTiIdRw7hnRUlwvdQe+4A7sbnw/H8tCVXJLkWgSbTWSveBz3c//9RD//OWIZdXVEDz8sUk2xhWalFKStp1KX78H8WkNDo3jcUKTP0kJT05Vtks34+MeJ7r4bnvjOnRLYLBRYze12yR0vs1l8PjdrpblZevjYbPCkiSDpVFXhfIkEXqfTWAtLSuwZE+GcnHff2kr0zDNEhw6B9JeXcUwohOvcey+MQjBINDAAySnXeNntMnqRB7obBrKV+vqIfvEXYYj8frzPKFZiKfZz60nB1GmbGhpXjxuG9HmUYDiM7wMDRIcPQ0oxg73qcBje7OQkfscZJoWkCdb8JydBtKzhX7qEBmgLCyLjcJbL9DQ+MzkJQ9TXB5mGCETd3Iy1V1fjGuwZKyW6vd0OI+NwoLHaM89IJg6D2zGk07ifWAwTt9xuGbHY14fP1tZKx0+7HQHsTAbnnJ2VJm65pFqsxLLW59aTgqnTNjU0yoMbgvTZA0wkiE6fBsl2dID0XntNCo24yCqdhn5eUyOdL839bRIJ0b9zpQmPB8SfG9RcWMDvIxFckwhEbLOBQJubQb7bt4PgeaA5d4QMBvGzz4d1XbwIGceci//mm/DycwmfCJ+bn8e12Ehs2wYDwxJOIIBjWZ+vqcFaMhmsZ3p6dQ7/+PiVQe1iWyMUkmKsjCtnMlVU6J75GhobiU1P+mYP0OGAF3v+PMissxMBVqWkdQGnHnZ14Wf2rrm/TU0N0VtviVQzMHAlqbAmbg5qMmkND0uny8FBSE3NzRIg3b8fxBaPi6RSWYnrJhIwVpcuyfxbTrGsq4OHv7yc/1lMTKC9cmMjDMj4OIje4cD9nTqF6wUC2A3wc3M6sc5gUOoD0mnUASSTWG+pUkohKSZX9w+H8Vl+lvy8zLsE3v2srGA9fF6dtqmhURo2vY9k7ufOfWGqqpDFcuedIM5wWEjU4QAZnz8PYjN789ksPHbOfe/pwevc7py5eeNMWsPDRCdPikFwOkUuyWZhLDweEG9fH4i3shLnYAMSi+G83P5BKRxXTHvndBqB4vl5pKLycem0dL5kEg0EQMSdnbhPv1/uKZuVlhRVVaUPkVlrsIq5//3yMoxVYyPem58neuUVGK+hIYl5RCIwlCMjqKiORHTapobGerDpPf1cr7GhAcSXSBDdfDPRxz5G9M1vSrCViTaRgNfIGrjZ4zd7tNxoLJdcGhrgqbNHy4Vb7Clns8jcCQZBXNzGgefiVlRIdgu3g2BiDAahszc1gRATCRzb1IS1jo7mfx6zs/je2yvFVxUVch0mXn7tcl3Z94clINbLzcHlYkiWDTFLXyyZmY9n3T8eB9G/+ipIPhyGAfB68azOn5fBNoEAKnyJYNDMbSc0NDSKw6Yn/dxsEZeL6NFHIbFkMkS/9EtIZeTiI9bx3/9+vM8N17htA0tB+dINzbKFUpIlFAjg+NZW6OiBALx5vx+Sjc+HWEMwKGRqblkwOQmi53YHExN4v6VF0kMXFqD1z8xgt2Luf8PkXlmJa/OOJRgE+dvtkKoWFqQPTk3N6rgB9/3h6l3OGlqrAtbcoI1bNUQiuAeWlqqrr2zdwIb25Zexs/B68buLF4l++EPsVpJJSHHhMJ7fzAz+zVZWtJ6vobEebHrSJ7oyW4QrVJnkP/lJ9M2ZmwN57t8vRM96PBO5eVSiUiBchlUGyfz86p1GayvOx+dobYV36vOBoMw7B7O3SyRSTyCA752dEtjMZrGWe+/FOfr6EDBeWMA6PR581usV7ZuLsMxZNBy4VQqEb5UN43aL5r9WBWwoRHTihBhNv18a0fHOa2lJagg4U4qf56VLOIaDxfPzuIdQCDLO4uLqzCrO0jJLcrpgS0OjeNwQpE8kMgQTs8sFkkmloGd/7GPi5XLrAc6dzyXyZBJkNTsLT5lJj71W7qFPJJ0kZ2eFIDs78RmXCxp0MAiStvKYbbYrpR7+bs5k4Z3B+fNSxHXgAOII8/MwcNXV8IqJkMW0c6fsHHKfU66UlZsNU0x6ZjiM4LLdjuuPjkKm2r1bAsTz89ICWqnVaZZ8LW5253Lh54UFnI8rhEMhGO94HL83DOyK1juVS0NjK+OGIX2GVWqfywUytNlAHBMTeC8YBLnnEnkqZd0/pqsL5BIOg6QSCRBQR4cQZDYLeYI917Y2vF5akiyYQgVN3MSso+PKz7lcINC6OpyvsRHefzyOfj4c6ORdzltvwTA4LP6Vi6mcLZSemc3iWuzdnzwJw8epnjwAngPGHE/gPkdcI+B2wzjFYvDsV1YQj2hvl8Z1mQyu0dQEuauqSmQgnbevoVEabjjSz0dmHGCdmVlN5tPTIJdwGKTF0kxjI4iGSLxgw8Au4dgxyQaqrRWd3umEjMHNzLgFs1UTt1xwd8pLl/A6GLzSc+V7i0RgTFwukODrr8OjjkZBgiwXuVy4h/5+qTpmsKHJNyx+LbB37XJJIzinE176zAw8c243ce4c1jEygvM3NkKfN6/h9tsRc6iuxm6GB7fU1uJ6XLE8NwfJx2yszf9GWufX0CiMG+7Pw5wOyH1x2Lvm2bLmVsaGAQIZHgYpTUzg6803ESzlFEomXJcL52NvlL1M9vKZrLlNAwdk3e61p2fx7oJz63PTJG02eLtsmKamQIJshLxeHBcMwmuenkYm05EjMAjrQb6h8uYCsFRK8ud37MDP2SzSXvfvB0krhftqaMAOJJ1efT6HA1JUSwvRwYOQiHiCWX09nntFhaSAsrHW7ZY1NErDDefpE1nr0dxjf3ISRNnWJmmcTMgtLfBKDQPEFY3CGHR3r/aCnU4Yj1yJh9MfAwGZIxuL4fdrodiKU68Xn7n1Vhi14WHJ/0+lcD3DgOc/NASv2+GA533bbXIuq9hHrjxSqMCKjSuTeyyG96an8Tz37gVpLy+D6Nvb8bwiEUhTS0tYU7417NkjhE6EeEHusJrm5tWxFJ23r6GxNjbkT0Qp9R+UUs8ppeqVUi8rpU4ppf77RlwrH2w2kVPMBNfbC1K8eBHeMhOFUiAlux1kVVEhkgsHZvm8zc2SP+92SxsFLp7iNgdEUjS2FoodFGIYIj3V1aEWYft2kavYUDAxHjoEueTEidXVvOaiNl6neYALPzMuNOPun2aPn7uPtrSAtO12GID778f7kQh2JnV1RG+/DYPA0lcotLoSOncNfF2ufbB6Nn4/DAR/6SCuhsbaKLunr5TqIqLPEdEsEf17IvoxEf0PInpTKfX3hmGcL/c114LZi3Y6EQxdWlqdo8/FVpkMvHxO1XQ6rxxdyBIPd63kVEzDWP+wj3zdKYmE5Dj46fNJVk42S/Tkk0T/5/9g+Dufx+NZXYnMOf633CI7nEKB3EwGOx1OMWWSNe88WJKqqYEEw9k1vMsiwnFzc0Qvvoj3HQ6sgQPWxbRWWKtzp3k9On1TQ6MwNkLe+SIR/QER/Q4RPUBEv2UYRlYp9RIR3U9EV5C+UupJInqSiKizs7PsC8oluExG0iSJQBD9/SCnmhppLZxOQwbKJRBzYNjcGZN/v95hH1b1Bvl6+09OynU/8AGs83//b6Lnn8fvQyFcm8cyVlRgd8IN48znscoYMgwQutcLIxOPy+AXRq4k5fViN8H9emw2xB0OH8a5Uilc8+JF7A76+mQgPU/x6uuzfmZrpZDqtssaGsWhrKSvlPoUEZ0kojOXf1VHREuXf14molqr4wzD+AoRfYWIaN++fYbVZ64GxfR493iI9u2TKVWzs5BRWBM3E0ih812tt5lbb5Cvt7+ZADMZrOPcOWS5rKzgHMvLQsQ7d+I9t1vSTx0O3COnsE5OYoejFCSh2VmRkziTxlwFnGtMuZUEEV43NiIgHo1iZ5JOyzziVAqZSl6vBIN5V2KG+Xnmg267rKFRPMrt6X+EiDqJ6BEi2kFEWSKquvxeFREV6BqzsSjkKeZ6iez555sFy4NTurpAgrnBYm5JYK4+LRVrBXbNOfScM+9wwFtmLz+dxjE8Q6C5Gb9fWEAGTTSKgG9/P0h9fBz9+t94A9k/8/M47rbbIMnMz0ulMVHhVhKpFIKvqRSkn/l5ya/ndNOVFWT25MpHRBKUnp6WgjmnE/8muZ58JiPDZrjKWqdvamhYo6ykbxjGp4iIlFLdRPS3RPQqET2slHqTiO4jSD/XDVbFRrle4soKPNz6eryfSyBWMgIXHg0PC+Fyu4D+/sL5+blrMevaaxVPMbjlQjYr3SiZRB99VJq9cfO3F15Axe7gIM79vvcRffCDIOWnn0aDOnPP/iNHiP7rf0Ue/eTk6pqDfK0k+H2nE4HclhYYG69XguNzcyIfraxACurqgvEZH0e6qWHg3ubmcJ577lk9A8FmWy0RcW0Exy00NDRWY6NTNv+KiJ4iok8T0Q8Nwxjc4OuVjFyPuqIC5JE731YpkP/YGF57vTiOyYd3BIEAiC4SgbRBhM+upTFbGZNCkhQbCKXgDVdXY07ut74lYxTvvpvonXcg2fzsZ5BblpeJvv1tkCrj7Fk0PXv4YaIf/ejKIS2DgwgSDwxY99jP10qCc+wnJ7H+ujrk8e/ahTRYLjSbn8eX2w0DY7PhmfFOoK4O1+FJYtu2yYSyTEZqIebnpTbizju1l6+hYYUNIX3DMEaI6KHLLw9uxDXKBasgb3MzPMzcbpSLi/CQ29pASk1Nq9MclcJXNisev98v6Y75NOZCmrSVJGU2ELyjIML6envhUVdV4R6Wl+Fld3VB7jl2bDXhMwYHcf2lpSvfI8L9Dw7CqFVVrTZ4RPLcgkEQLz+LpiYYE84C2rZNdHufD/d76ZI8c2613NmJ9XJfIZsNn+cpYOEw+u4nEnjW+/bhmFJqIzQ0tiK2vC9kVcHLQ056ekCWCwsi7Xg8IEabDeTJufk88YkJaWVFpJ/cHPhcFMqZN9cbEF05oMTthqe/soL36+qQfup0Qi7hXvxMglajFolArOb6glxwF82WltUB5EgE2TgXL4Lwm5vx3FgCcjqxVg4iOxwis9XUQBLjY2trQew80pIHyNhsYmh4AtrMjBhejwcxCl4j0ZVtnDU0NIAbsiK3VOQL8pq7UTocIKD2dkg83AisqUk+39cn7RcyGUn3XKtFQCn6fa4c5XZjDaOjErglWt2lc3kZpJ9MYv1W4FGRfj/GKprbJDQ1oQjM74eh4yBxfb3M1eUB7JOTeA6595RIyLwCIplStm2bSEThMIg/HMaX14vrOp342e+XtthjYxLHaGtDttHUFILT9fV4HjptU0PjSmjSvwxz8NU8QMTct51JuL0d+rhSICLW191uaSPMA0xYIipUpFVMSqlZw881EJWV0LAPH8YXa+633gqPm5utNTdjtsDQENFzz61ew44d+LxSuP/BQQRPiXCNV1/FvWzfjuu5XCDYzk6s89Kl1W2pfT6Re5aWIMGYyZjbVPv9IO3paSH9e++V4fS8U6itBYFzIzcOnPMz4Gs2NubPutLQ0NCk/y+YmUHVaDgMieTAAZmKxdW6fr+Ql90OUltehvfL8kxXF8inmCItc7ZOKSmlNTXwktlANDcjGDs5ibWzfBIOYz3d3fhMKoXdyl//NQK3zzyDczc1IRaQTku+PXf7JIKufugQSPbOO9FX5+67QfATE9KllAhG85VXYPw4V39iQoaz5LapTqVw7y0tIHaeFcCdSzk4GwqtDs42NuK9dBr3VF8P42AeVq/TNjU0roQmfQLh/8mfIEOFZ9vefz/R44/LMBClQJ433ywDx4eH4VlPTYm3PjoKT5UnZeUjnHwVpGullKZSIHxzjUAqRXThAjzqHTvggYdCCOyyTm4O3rrdRPfdJ8Nfkkl8z2ZxzPIyjFwuJidxjZER6TlkGNgVVFXB+/d4sD7u18OFX9zugkiMT25rZy4YY0ktX+O6TAa7jYMHpSaCO3uu1dJBQ2OrY8uTfjqNdMYXXwSRcHbI88+jvW9jo0g2s7OSrTI5CR359dfhFSslbQVaW5GWyMVCud57KRWk+Yq0DGN1TyBzXnpVFTzv6moQYiSC8+/bh/NMTEDicTqRhnnokHQgjUZxrFXQOZuFMTAMXGPHDhgX7kXEWUp1dfi83Y7ny3EF9tq5K2k+g2iVUcVN11jaMQysNR7HVzqN92dnZdxjvpYOGhpbGVue9OPx1VJGPC7FTpcuSdaIWS4gwmdOn5YMHvZqub8M974PhaTXD1foFttGmUgIkDVuji2YPVinE7LI7t2oqGWiveMOKXziIieHAzuW5WXc61tvQRqKxxEcnZqCp24FpxM7B4cD9zw1Ja0q2IiFwzg/9w3icZUTE9LGurlZ2jXka+1sFeNIJPC8l5fRtfPFF7F27i3U1YXK4ZYWGJ7clg4aGhqa9MluB7G73ZA2YjEQIA8rOX0aJMafZbLl7BO/H6SayeB1ayskkPPnif75nxGkDARAlskkeuBwsVcxUgSnNr71lni+AwOrjYPNBq/70Ucli6ahAeSaTNfchVUAACAASURBVIJUGxqIjh+X7J2aGnyPxWAk3n5bhpCnUvCiOROIr9HeLoFc1tTn5yUldGoK97S8jHvnNFYeIVlZuTqA6/NJkDnXqOa2uSBCa4jZWQlEDw1JBXIwKLsbhwMGob4eBqCiohz/UzQ0bgxsedJ3u5G1smsXiDWRADkNDIjHOjQET9o8SKWrSzpxMlwuyWo5ehT6fm8v2hyEw1JNms3ivMGgaNu9vfmDvQsL4gHz6+rq1Z9nD/qTn5Tumm+9JcYlGoUB4N1ENCpGSykQvrmVAg8lDwRAurt2wSD09+OefT6cw+1GiueJEzAAlZWIL1y4gPvNZvG77duJPvEJGIt33sHxHR0g6EBA0lSTSTyX3DYXPASdYwbcciKZlIymhQUYA48Hnz9yBMNYPvMZPHcNDQ1N+mSzITh78CDIeXISJOPxyHSt6mopNmJyCgSQxcIeLgcUOzqIXnsNnn4igWPefRcevtcrej7n13PzNpYicmMAuVIQkVS3mkk/k4EB8fuxxuVl/O7CBXjxoRDuYds2eL/nz+OanZ0gVm4zwYS5sIDdQU8PCJtbI6TTWGNtLYziyIikiRLB0J04sXrYytISDERFBYxHIAAjxxIOkewKgkHrWAeRFGvx88pmcd1kUuQqhwPXaW7GWmdnib77XaLf/m3t8WtoEGnSJyKQ0B13gCCamkDaRCCgm24CAaXTkpppt4MQt22DhGAeVvKXf0n07LOSoTI6imNcLklHHB1F3vv8PF7v3g0SdTqlnTF7udxVMp+mb87fN3vJly7hmHfewbVtNnjz3/wm+u+Hw9jNhEIwAnfeKQFdBjc+6+tDQ7b2dskE8nhw7889h3x+vx+ku7x85TzddBrX5pGHdXWiwbe24rwclM4X62CjEArhWfX0QDK6cAEGio1OJoPrxeMih8ViMJSa9DU0NOkTEQhx2zaQ4/AwPH67HV7j/DxIL9cDnZ6WSVyGAbIZHARxsuRABEJ65x2cc34ewdKvfQ0EGI3Ce37zTRBpbS08795eHMuEm0/TN6d9cpM4blvAa0yn8X5rq+j08/MwWqdOwcgtLeH9fP1qhoZgLD7/eRjBykocOzKCa7rduGce12iFVErux+WCzPP22zB4U1NYNxsuq8pkm00qnisrRa9/6ik896kpXIfvn+fwJpP4d/L7y/pfRkNj00KT/mV4PJAr0mkEdokkP5z7zOR6oI2NCI4ahowkTCbxMxcNZbMgHJcLpPaTnyCT5eRJfCaZxE6DWxL4fCD+9naso6YG5JWr6QcCV7aEXlyE5s5pp0ymqRTWUFGBc7a04J7CYZGETpzI32yNCIbi+9+X7CQm49ZWeOznzuH65iErZnB1bVUVrmdOuayuFpJnucaqMtnjkfTZTIboxz+G9MQ7DE4HJZJnX19P9Au/oL18DQ2GJn0TuF8Nywn83eWy7o1DJPnh6TQ8dSZmPp6blGWzMCATE8gIYpkolYKk4/Ui3dJmw/eWFvx86hQKxZjMnU4QPAdlzS2huWiMJ1S1t8MrfvttkLbfj/Ny58rWVhi6TAYVul6vtF6wQigErzoeB3kPDeF8nZ0S2xgZsU75rKjA9ZeW8Jw4yBsMgpi5OZvTKa0drCqZzQVvHR0gejacPJWrvh599w8eJHrkEZxnZQX3p6Gx1aFJ34TcSVBEkmGSWz3a2grSm5+Xit1MBtW4hw9L18vGRuljz+mIXGhkLoKanwch8nnefVeKxH70I6IvfEEyZxobrzRELOFkMiDedBqEzkHN2VnZAXR0iPbuduN8dXU4v7lmwQqLiyBYxvi4GDyHA9exIn23G2sJh6Ub5223gagvXsS1OZax1tAZLtDyepGqurSEY5JJGKOBAeyann6a6B/+Abul3l7IU3v2FPEfQUPjBoYm/Rx4PCAkJr9gEGSaC27v294uso7NRvTpT4NY3n0XhNbWBm94504Ygve/n+h73wO5x2IgunQahMlFVGZdPJWCp/6lL4H4/X5cy5z9wlJIby9+Hh7GNWdmIJ0kEugltLgo3UL7+xFfmJ7G+u+6C4QZCkF6ykUgIPfi9YLsudXC/Lx45flaN3NhV2MjzsGGMpWC8YzFYKQ42E1kXc1sjmOwh3/nnZKrzxO4/vN/Xn39vj7sYv7qr6RiWENjK0KTfg5YcuAgLQcgOWvGPECEO2u2tYFYAwG0/t2zRzTu3l7R+JeXQcKPPw7vPZmE58uVstx3xgxuPXDxIsj1lltAfJcugbjNTdr4cx4PiDkQwBq4A+W5c/K5+noYrGQS52hqwvruuQfZPaEQvPaxMWjv1dVEt9+OtS4u4hmx1JRK4T7sdryXC87F57qGQADnfu45EHBTE847PS1BbKveRDzUneMYTieePaemBgI4z1e/euUahobw/unTkH10ewaNrQpN+jmwapEQCsFr9vtBQNxDnzNfWMdvbwc5+nzwYpeWxEsdGwPpcmHUI4/gmNdfl3TM0VHRqDkgyXn8mYxo6F4vyJI1fs7kGR2FMXA6cQ0eWFJZiUCtYcCrr6jAZ1taQL48ECYWw/r7+nDPwSBqETwenGNiQoKwhiFDzbkaNjdVk+FyYafT14fPvfWWZP2kUvD+OWA9NITnODoqhoLz9dvbr5wl0NGxOmPp619fXTBnBsdidOdNja0MTfo5sBr+sbAAonG7QTDj4/A8t28HGXFhFlfs8tCPbBZEPzdH9POf4/36eulXc/vtOP+FCyBQHvc3Obk6BZF73CeTIMN9++D1mweScMEXDybPZnEdHvuYzYLsGxrwGdbEHQ7IObOzuEZ1tRBuXR3OYbfjPHV1Miry8GEYgUIZP0RS3Ob3IwhMhHvmdtXT00hpTaVgCJ59Fsckk9DrDxzAdVnyyq1Z4MpepxPr7umRIq5c8Ll0502NrQxN+jnIbfbF5OnzgaAyGRlBaE4hZI+edejGRpBQbS2MBrc2IAJR8vzX3bshB/GwEe7fMzSE47NZXOfee2E0vF58rq1NiI0Js6oKu4/paXj62SziE5xKyZ7upUuyAxgbA+lzBW5bG4xDdbW0MA6H8TwOHcLnzp6VQPVa4F79ySTI/OJFGDwefchzhHftwnXSaekb9PbbuI+HH5bB61Y1C2xwx8akKyobGMb+/Ujd5D5K/Cy0x6+x1aBJ3wLmgSZKgXwdDnjii4sg/dlZ/GweyZc7sLy2FqTJDc24wCuRAHnzTsJuhwfq94O4OYPn9ttB8C4X3puflzYDFRU4LhaTtshzcxJsra3Fz8PDRC+9hHPy9ZRCv6HvfIfo7/5OMpVaWlBE5vUKKc7PQ4L6yU+KJ3qGz4dzXbqEdXB2TiaD+AHLT7EYUlN5jm4kgufb0SEzeO+5p3AfIr8fZF9djd8dOoTnXVeHlNd77gHhc9wjd46BhsZWgSb9PDDng5vTODljh5uEmXvDmIOM3CCMPfymJsg43BUyk0Egk9smVFfjPKx9NzXBy21pEa+Vm6CdOYMhKHxNlwuENjaGHUJPDwhzchLafGurTPjiwO34ONIZzcNJpqZQbPb44zj+9Gms8dSp9T3DhgYYRq514GlfPNuWSOIDsRjudXFR2kHzRC27XVozJ5OQyBjch8jplA6i990HozY1hTW4XKgk5vYUHs/acww0NG5UaNIvAuz5cxdKzlPPbQfM2TwsPdTX42efD+TV2AgCtdshkUQiIHafD8ZEKZB/TY1ID2fPwjBwszKeRPX88xhZmEqBQHl6F8+o5RiA3Y5jl5ex7rk5GAfesXA2D4OHqESjRC+8sH7C5+rfeBzr4mfHbaV5wIzNhnW43RK0jUaxO2prg/Hz+XAuJunKShlAbx62znJbUxOeE8/y7e7Gd97RcJ+ftUYqWqWMamhsdmjSLxI2m+jKVr1hiCTnnKWH+nrknTOpnD0LIg6FZAJVNCptjkdG8PX+94OkXn5ZsoFSKQSD29pAgK+9BgJvbYUE4/OBSH0+ECNfd3IS5375ZRA9t2EwD1g3Y3YW8sfoqHW+frHgGQU8vIUznzj7JhSSjpl+P3YWqRQkNK8Xnvm+fXg2bW14fm43nmkiAcOUO2ydx03yMPXmZtx7LIbfd3TAgOb79yMSordq8axlII0bAZr0S0C+iU5M8pxpQyQZJKxZnz6NzJRTpySvfWkJ53E44NkmEvDIL1yQa1ZVgaxWVuC9zs2JfOTzgfTm58Wr5lmxRCCumhqka7KGzsNhKioQYD1zZvU91tSg7XK+HjrFoKpKBp93dcHYpNMgd65bGBmRYSp1dTK4vbER69+2DRJUd7ekY5qzdYaHsftyOnHf3MyNSCQvjm0kEjJ/N9+/H5HEZHhH0dp6pYynPX6NzQ5N+iXCHOQ1b/u5fXJvr7zHU6ReeYXo2DGQczwOWScSwXtE+TtTEkm3SCKpEUgkYDz8fpBaRQW8Y9bGPR4Q3OIiSJTbE0xMQNrhAejB4OprcV68WY4pBdXVRB/+MILBPh+u19GBNtLJJO6/vh4EWl8vGUdM2n4/jFpFBdEPf4h7q6tDnINbTw8MyHjKbBb3w+0lmppkHKU5l5/bXBf69zPPLeZxkNyvaC0ZSENjM0GT/jpgDvIyWCbgoGIqBeIYHgahzc1BakilYAxYXy71unNz8JrTaZz74kW8t2MHyLKzU9oMxGJo23zkCDx6viY3hqusxHduVMZQSsYYFou+Psgx/f1ixGpqsM72dnzNz4NY5+dBvpGIBHc5sMrdQnlASioFY7p3L9Y1NwfjpBSea0WFtJaYngahWzXHM0s4Vv9+5qI87knERF9onKWGxmaDJv0ywUr6aWgAKQcCIKdYDF57KiXB31LAw0jicRBaMIjXkQg0/ro65KOn09C5vV5o+W+9tfp63CSuq0u8+kgE5Gm3E330owgMDw0Vt66+PqIPfUjSKxsbQZo+n9QzEGGtdXUg89lZXLe2FkHs5WWsNxbDfXG7i3gcRD88LINamppwnrNnJWbA7Ri4e2g+CScfcovy6uqwU4nF8LqYc2hobAZo0i8jcqUD9va5K2Z1tQRuS/X0eRfBxyWTV3bEnJsjeuYZfO/pAek+/7y1gUmlJK8/EABZ/v7v47P796Nh3NIS4hCFoBQ844kJEDjLS1z5OjSE4rNoFOcLhfAek3IshnVWVsIYmQPLmQwMBVck80jHI0dg2GZmsFPYuxcGi5+T02kt4RRCrtF2ONDIjbuZmgvvdDaPxmaGJv0ywywdsIc4Pg6vuq4OHngwCNJiuacYlLIzGBkBMb37buGCKg4cezzoBdTQgHW53Wgxcffd+JnbMg8NgbjNfYHsdpyDdzTcGC0QWJ2Lz11ImbzZCEYiksXU1ITXfr9UA8diON+5c/DsFxZA9jt3ougqkUCg+q67pA//esk5n95PZN0ATmfzaGxGaNLfQNhsCOxyQ7OuLpDK4KC0Pj5+3Lr//NVgcREe9Vp9cT77WWTJGAakFda0V1aQQbSyAi/dboes0tUF4h8bg1dOBN2ePfK5ORA0V992dEhXTh5vGAphh7J7N+4/GJSUSh6iUlmJdQQC+OIMpYkJMapLS4hXPPII3mtrwzFXS85Wer85yKuLujQ2OzTpbzA8HgRZa2uJ3ngDpNTaiuZf8Tg81gsXQHaHDwuZXg1SqbUJ/6GHkAnDYxTn55GXn0ggRz6ZxDp9PpD57CyCtPv2Ef3sZ2jNsLyM3xPheA4gKwXD4HJBm19akiKqxkacLxzG+W++Gdk9LAc1NyMgW1WFnQCff3AQRO7zSRfRSAQGxePBPXAsw+0uLzlbdV7V2TwamxWa9K8BuA3zgw9CplAKhBkK4fd33SXzbb/2NZDeWmhpgUd8/rz1+0yWVujuJvrgB6WX0PIygr08xOXCBRnUwh1EEwl8PplcnW5qxsmTWNOOHbhnr1e6jfr92NEsLIA0w2HJ8unvB/nzIHOenXvmDD63d69MB+MCudOn8X1oCP3/eRTl6CjWwOmb5SDn3CCvVUaQhsZmgSb9awiXS4qO/H6Qe20tyD+bhUTx67+OJmj5iL+mBuTb349zhEKl7Q64XfLPfw5vnQgk7vXi+tXV8I55LKPHAwLevh1G4NlnsWOxQioFo+b3S4vmnTtB6Nw+ubMTXzzdKxAQQ3L77ZC7uOHc3ByMEGcbRSI4TzwOqcXpxOuZGUhJt98uraWnp6Vvz9WSc6GiPCvogK/GexllJ32llIOI/omIWonoHBH9FhF9k4jqiegVwzB+r9zX3IzIZEBmbrf0v5mbg6zxkY9gGIhV0dbCAiSOaJTo6NHS5aC5ufzDzzs7QZockOUq2sZG7A4mJoi++93CweepKXjht90G8uZ8/IYGae7m8SA+0NuLdE8e8ciZP9x0jesIjh7FLmBkRAbCO50wMPX1ONeuXXiWd90FQxgOw6ByWurVolCQ1wwd8NV4r2MjPP2PE9FJwzCeUEr9hIh+k4iOGobxZ0qpHyuldhqG8e4GXPc9Dw4IcuYKD0dnGWVxUXL5q6vze/vj4/gqN8bG8L22Ft4598Dv6oKsEo9LMVg+cNdMvx/GY2gIOfwrK3JPPh/uu7kZz4LrDhwOEDfPBUin4fm/8YY0YksmcR1zo7hkEtdraECaJbdf2LZNUjmLwVoeulWQN/d4HfDVeK9jI0j/GSJ6+rLHX01EHiJyKKUUEVUQUTL3AKXUk0T0JBFRZ2fnBizpvYHcqs/2dvTZX14G0Xi9IN5Ll/KPHrwWWFyE/JNIwEuvrQWBvfZaccdzczSeK+BywWufnhaNf+dOvE6lsFvp6MBnHA5INouLIPNYTM5j7gfEKawrKzAiY2N4noOD2JX09konz2KklnJ46Drgq7EZUNJ/RaVUjVJql1KqVSlleaxhGBHDMFaI6BUimiai/0ZEHySid4norGEYV9R5GobxFcMw9hmGsa+hoaH0u9gkMAcEbTaQ1I4d8Gz9fsghXIB0vYKE7M1OT0vq5fg4NP3bbivuHByk5jm9Z8+CfOvqQITbtyN+wOMlm5tFi6+pIXrgAcg+ra34HMs83MzODK7azWbR82f7dhgZtxtEfvGifOUriDN76H4/vk9OlmZ4s1nJUGL5Swd8Nd6LKNrTV0r9PkG68RHRXxDRg0T0KxafqyOiCBHdTUQvENEfEtGXDcP4W6XUPyml7jYM49VyLH6zITcg6HSijTJP04rHQZR9ffBgcxuibTScTsg4hoGfOzrgbcfjIPJYDGtbqz3DygoIz+3Gsd//PoLGjY14fewYpJj+fhBlUxOOqagAidfXYwjK2bNS1cutIszg3VFlJbz99naQdiQi0koxUkvuDoxIWmUU46Hz9DKONySTeH7FtoDQ0LiWKEXe+bhhGAeUUj83DOMflVK/kedzv0tEZwzD+LpSaoWIPkJEX7z8XoKI/Fex3k2PQgFBnw/vORwgvurq4iWV9cDrBVHW1iIAy95qNotrNzTg9ysrGJf49NPQ2dcCVwGfPQtCjMUg13znO2IwlJK5tSsrIG6lQJK9vTAAO3bguO5uKSDjoSx+P8i1uhrPascOyGJtbaL3cxdPosJSC3vjy8uQmhIJKS7jgSv5kM1iRxQKSTUwB5CdTk34Gu89lEL6i0qpXyGiCqXUfUSUL2/kfxLR15RSv0lEQ0T074joq5dfjxHR81ez4BsBVgFBHvenFEhwZkaqTXOHfJcLbjckm3QaxogHjlRXg0RZU/f5iJ56qrQe+z/9Kb7ny/QxDBi0mhp85uMfxzNIp/Gdp2o1NRF9+tMg1YkJqSXgCWC7diFw63IhOByJgKxz5wZzMzmWX8wGlyWmY8fwvtsNKSoYXDsIyzuIQEBy+INB7GLWS/g65VNjI1EK6X+WiP6AiBaI6DEi+jWrDxmGMUFED+T8+n3rWt0WArds4OEfu3dD6hkc3DjSX1gg+va3V//u1lshOfX0gGhvuonoxRdLH6pSbE+hYBCGbWgIHjJn6LCHPTGBVFKe+DU1hSBwIoHU0EBAisdSKdHjKyvxPMfHoeebex/lBmtZi29uhoFjsuX5u2sRL8diiPCcFhaQTtrWBqNWCnHrlE+NjcaapK+Uutf08ikiUkRkENEOQqBWo0zgGa/8B7+4KPN4rxXOnEGR04EDkmOfj/BbWkDaVzNla34epOzxoIEaj1Jkwpudxa6nsxM7oZYWkU28Xhn+XlGBdfNUrkAABqCnBzuA9nZ81maTrJ7JSRA9j1AMBiVwXGwQ1unEOebnYaxefhltKgwDa/i1XyP62MeEuAt58TrlU+NaoBhP//7L3w8SUZaIjhPRXiLyEtG9+Q7SWB/M0g8PC2fZYKOvy4QUiYBEPR7o3FZZtA4HisjGxtAzKBpd33UbG+EZv/oqZJnWVpC0YeCemawTCZAyD0p3OrE+t1uGzrtcuIdEArsjpxPn4aEx3K+HSdfrxTU8HiHliQmsodge+jYbDLXTiVYWhw7Bu6+uxrP7+tch9dx6K9ZVyIvXKZ8a1wJrkr5hGP+FiOhyAPch/r1S6tBGLkxDtObWVsgSGwnOWnE60etm2zYQ6vIyArpjY/Bguf/MY48hRfL220HElZVo5fzmm6V1DR0chJRVUQG9Ph4XuWZ2Fl5uS4vMCeZunETQ/3l4eTwuA9SZINNpaevMg85dLmnONjMDo8Ln411VZyc+UyzRejwINg8PwxhWVeF7ZSV2IaFQcdlEusePxrVAKZp+Sin120R0kohuIaJ1zH7SKAU2mxQabTTpE8Hz/fCHiR5+WIiJg6l//udIu5yYgBd7662rM1Z4Pu/YGMi50NxfM5aWkHUzMIBzZ7PIta+shOwzOAijNzEBD9rlEjKcmIBh4jTYixfxM0/nWl4G0SeTIORLl0D2djt2Br29sjPgWbqctlqqZ80yT0UFdiqBAL673cguIlrbiy+1x4+GxnpQyn+nJ4jIRUS/RJB2fnFDVqTxL+CRhpx9slF4+GGiP/1T5NN/6UsgrEgEZNncDPJxu4nuu4/o8cehvd90k2TErKyAvAcHIUe1tZV2/bEx6fmTySBYyxW3NTUi0zApptO4XiIBT93ng8TS0oLnFYngvZERfE1NQdrhXkfd3SD78+fx2bNn4fUnk9jVrAc2G57JZz6D81y8iO+f+Qx+z3n7VoVb2SxeZ7PSl6i1Fc+ejaqGRrlQCpU8RgjczhACuR8moq9uxKI0AJsNXu9NN4Hc5uYgY5Rb33/2WRD45z4Hr7S+HmQ4PS1kaC444gBuRwe+BwIgVocDuwAivDczU7zH/4MfgPjtdpD4XXchHXNqCufo7cV7LhdIkTttsjHktss88P3cORxXUYEdDMtAMzMwIJEIZJgLF2T+wLZtsrvp6Ch9OLzHQ/TEE5hVEArhOZqzd6y8+Fydv6YGr4eGsJOpq8O/f39/ccFgDY21UArpcxG8h4geJaIQadLfcAQC6MN/6BBIP5HAH/3MDIilXHj6afSl//M/J7r3XujpLpdoy8kktG7ua89BUc6i2bkTnj43gquogNdfV4fPsEafD/E45BDG0BB2DC4XXtfXE33yk0S/9Esg8GgUXjvn9TNhjo9Lq+qGBjknZwTxTGCWhnggy+HDOJbnFFy8SHTPPdIeoljYbLhnHihjRm5hHhGuwzp/IoHmcpEIAttcMHfuHOYf3H772sFgDY21UDTpG4bxj6aXX1ZK/c0GrEcjBzYb9PMnnyT66lfhfdtsKNyqrETv/YmJ8lzrzBmiP/kToj/7M+mlTyT6MwdJs1lJk+RZuOPjmKn7/e8jZ52zY7jgqrMTBJ67Vrsd5GxuOeFwwNCMjMjvzp9HkPjwYdw3j1V86CHITk4n4h4uF3ZHCwswJD4f5CeHA4TPhsLjgccfjyPrZnxciqr8fpw7nUYfICuPf73etjnQzK0e+DlzhhHPCKirw2dmZrDGm2/GGnVKp8bVoJTeO+b0TD8R7Sr/cjSs4PEQfeITIKCxMXjBFRUg6Y9+lOi558pXwPXOO0SvvIKArrkfjjmLxDBEAopG8Zk9e6BB19dDMnnhBdQZRCIg2qUlSDR33421RiLwZD0enNfhAMkqlb/VQyyGSt+Pf1wKtb79bQSaub7B44EBuOMOrIOH1DQ2QnpqaZFn+s47mPY1M4M1JpMg0ZYWeNBzc1jrrl2rYyrlKqDKzdZhQzIzg+ejFAynzYZnwr19dEqnxtWgFHnnftPPSSL6t2Vei0YB2GyQL6qq8PPMDFolTE0RPfooPOHTp/H6aoqllpZAhjffjOu4XIgn8IB3ItHdufsl9+vhRmltbfBIjx6V3vgjI/hMYyO+ODg5MQECXVyUFMtCSKdBkuzJJxLw8B0OGB8mUKcTa85k5Hrj48jicbuxxpMnRaLiY+NxrMHnw5rGxvD5ri6p3i2mgKqYnUButg73HmpokC6l2SyMKUtd3EZCp3RqrBelyDv/xfxaKdVT/uVoFAOl8Efv9YIQmpqQN//uu5A/3nwTXu568eKLIOk9e6Brt7aC+BhmsmLiaW0FEba2QqcOBHAM99bp6oKReOABrLuuDkQ5Pg5Dc+QI0f/7f9CvrebvMtxuKb5KpUCQHg/Oc+edMAYcKOWAM6+9q4vo1CkYMSZyTjtNJkV6aWkB4VdV4T5sNpA/Z/2s1citlJ2AWefn1NfHHsN9cZB5zx5JW80NquuUTo1SUYq8803DMD5l+tU3iehA+ZekUQiZDDy+3l7J4uFWCJWVIGmbDWRWSpGUGcvL2Dnw0PSKCmjdTJ7ZrEzUMozV3qzLBf3+oYfgSTOZBQL4am4GaXLh1e7dILGqKuwS3nkH1zx+HMFrc6VvWxvSS0+fFu8+GiX63vdwbFcXsm44y4d78HOTtrExPJeaGhgdvx/HjY7KGMp77sHvR0fx+tIl7AZCIayZ2zYHAjAenLbK3vZ6WimYYyV2O879wQ9CCksmZcC8VVBdE75GqSim904nEfUQ0S6Tru8noiKa7GqUG0wwmYzIFPE4vMtUSuSOtVoCW4Fn2trtkFEmJnDO+nqQYWOjxdvNswAAIABJREFUtRdrvpaZAP/VvyL64Q9xDsPAZ//wD6VIamCA6POfx7lnZnDtO+7ANfbtI/qd3yE6cQJe/J490NZPn8ZXR4cMSjl6FGmNqRTRSy/hmXDmEGe7TE3B4PDM3Lk5aPzBIIg/k8Ex3d1Ye00NjB738vH7YYiamsTwDg/j8zwMhujqWimYd1CZDIi+qwtr5GecG1TX0CgVxXj6PUT0fiKquvxdEVGMiD63UYvSyA+rqs3OTpkt++KLUrhUCurr4UGy4eApU4uLuEZTE9Gv/zpSIAt5sVzBOzoKI/HQQ/i+soJMo/FxEKjLhaKov/1b/M7thjyVSMDD3r0bTd/27kUao80G8o1GpecOB34TCZD4978P2SedhlG45RZIPp2dMtSlsRFFWtEojEJXF54bxxjGx6VqNxjE92hUYimBAO6F2y/zs2dcbSsFq7RO3q1YBdXzQefya+RDMb13XiKil5RSvYZh/PE1WJPGGsjVgUdHQaTNzTJ6kAeIrwUeXpJIiF5sDqY6HJA+XnsNxUs7dyIjhgiEwnKHmfj8fhA/6+kckORslKoqKYKKRnHts2dxHYcD93X8OAjW44FBWlrCMek0Xs/P4/qxGM772muQhswD4wMBol/9VewSpqdxb7fdBvKsqIBx8fmwU5qbk8pfLurq6MB7/Ey4BQQ3duOAsRnlaKVg9uJjMWk2x7slc1DdCro9s0YhlBLI/exGLkSjNOTqwKkUCIzTImdmEFBdC+aCqFx4PCBOpUAi586BADs6II3MzwsJtrevJhYeRtLUhLVwEZfdLqMhmTgrK0HqXCzFGTpMwHNzMGY8SrGmBqmas7M4tr9fZCAzlpeJvvUtMRahENJdGxpwrv37Ic+8+Sauv3Mnzj02JgNkwmHcXzSK4+Lx1VXHVuRbaDpaKeD4QGUlrsmZRWYDm+8YncuvkQ8b2NFF41og17NsaIAsMjRUHOmvde5YTAaDHD2KPPmlJZFuPvpRkKKZWMzB5kwGpBwOQwr5i7+AJh+P4/cPPggidzqlB75h4Brj4zAso6Mg57o6kF9bG9FnPwty5mNOnLC+h2gUAeWKChgPDtoyOC7S2wtyra2VgSg8/WppSTJmfD5p1lbIey6H5p4bH/B61x7sotsza6yFYgK5XzQM47eVUj8n9NwhujxIxTCMBzZ0dRpFwexZKiX95n/wg6s7LxdesQb/9tsix1y8CE+7owMVsWZiMQebWdeuqIAn/Y//CDJnTz+ZhLTT0ABSDgREt/d6QezpNAKxc3P43Ec+gqDu4CDet9lQs3Dy5JX3kMkgcDw2Zn2PiQQCssPDuKdPfQqk3tQE45PNwjBUVMDwRKOQWrxerKeQdHK1uvp64gO6PbPGWihG0//ty9/vX+uzGtcPZs+yvR1k88QTGEZuhcpKfIaHmFu97/eD1LgR2ugovns8IOuREcgsPT0gSSYWngNw6RJ2CuaBJH4/Mm2IQOaDg0QHDyKD5o03QML19bK2aBQ7hPPnpflaPI5pVE1N+H0oBINy4ADy/Rmsy58/X9wzHBpCncN99+H+tm3DzmJkBMTPw9OVwu7CZssvnZRDV19PfEC3Z9ZYC1reuQHh8YCw/viP0TnzW98C8dlsIKjWVhBnKET03e9ad+0Mh/H56mpkuFy8CM+XM1tYhkkk4GF/5jOrg4/mXjrNzdaEx+2S+ZjeXpD8XXfBix4aggc+Po5dRSAg2vyLLyLlc/t2yDBEME4+HzR6nqxVKl5+GdesrYXRevxxaTFtGCD95WXcv9MJI8ZBafboufqXA9eZzPp19fXEB8oVU9C4MbFu0ldK9RiGcZWqscZGgYunnE7kxq+s4OvoUejfXNxTU4NAqxWWlvBZNhSNjSDfZBJedHU10iLb2wsXJwWD1oRnt4MYJyZwPiaohQWcM5mE1//OOyD7QADyTjYLrX9hATIPEQzEyy/jvd27YUTm5nAPpSCdBmHPzEjG0cMPw9v3eLAz6e6Wtg3crZObzLW2SrM4vifO7V+vrp4bH8iVjaxkJJ3Hr5EPuiL3BobZ40smoVdb6fzc08Uqt39xEX14tm2DETl0CPq41wtZ5n3vg3fNLZBLCSRyTv/IiJAmS1MuFwi9pQU/v/UWSL+6WrKBZmawPqdTun5ywVY0Cs/f7Ua6ZinIZHCOkydhNI4ehfw0NSXjGvftQwCYiZ3n7Y6PSxzA7cbuYHwcxiBXV1+P5p8rG9XUwPjp9EyNYqErcm9wMJn8r/+VP7C7Vk5/ZSW6WBoGMnZmZ0HkNTUgu4EB6UJZaiCRq2CVkuIjPg8RcvqfeAJk/8YbiCt0dcGbX1iAFMOdKR0Oyal3u2GMenqw1qGhkh7bv4Cnb73+Oq7JXS+PHEFAmds2833ys2xrg7HJZPBeU9NqYl+P5p+7i0okYAx7enR6pkbx0BW5WwDRKNH//b/rO1YpSCqvvgq5pK0Nr5ua4P1XVKxuO2wVSOQce37fDJsN3v3kJAg030Spj30MMsvQEFI3x8YgrXCffDYYi4vwrD0eBHcbG0HETU24h/UinYbM5HLBUA0PI5bQ3o7GaBUVIOXaWpng1dICIlYKxzDWm0ufu4uy2SSeQKTTMzWKg67I3QLgiti1wJ65GR/6ELxJ7jq5sADJIxIBmSt1pX6cKyvxqMJ8Hu1aE6V4kEhXFwwOB00jESnq4nbIAwOQgWZnsROZmJCdxy23YKdQqItnIaRS+Kqqwtp47kAmg7UYBt6rqUF6K+92BgZWP5/15tJb9d/n77w+nZ6psRZKqshVSjUQxiUSEbUahnF0Y5alUU74fNDl8xUwEYGobrsN3vz4OCSdhx9GK2SuvPV6QeKRCNGxY/B8Kyqg6+/du5rMmbyKnfRUaKIUkyK3IZiclCHq3BKhthbedH8/vs6dw7VdLpD80pIErnkMpMsFCSYeL+158s5haQk7D6cTz4wrk/fvl3vMZvFMuXbCXMdQai691S5qYACGOBK5PumZusfP5kMpgdy/I6I+gsyzQkRZIjq4QevSKCN8PujiL71kXaTU3Q292uOB1tzSAtL6wAdk5GB1NchubAzestcLr9swiJ5/Hue5/XYYAUa5PFozKTqdWBsT9ewsiNvhgJfd3o41sa5fWyuZOHV1siavFyTNfW3M4OeQD6EQ3uc4w+nTKA5zOPB7nnNABFloZEQynnins95ceqt0zOrq60O8usfP5kQpKZt9RPQIEX2NiD5FRM9vyIo0yg6bDQVHv/M7yMsPhaCfd3cjKyWdlkKj2lqkSd5xBwgplZK0yLNnQZT19SCaUAiEefo03jtwAHntbW34fDk9WjMp8kQvrxdxhf5+6Q7KKaXT0yDeWAzfJyZEholGcc88cCUXHg8IuqkJuxyrlNZoVHrpc/+h5mY819lZ7DQqKrADaGyEQTLn619NLn2unHY90jN1j5/Ni1JIP0JEH7j88xNEVF/+5WhsFPx+6PMHDuCPkwje8PveB2J8+mn8LpGAzLNnDwisrg5Sz86dINOGBnS0tNsRzJyawvsdHTACP/sZUkM5hbOhAQRcDo+WwR4mz7Str8fn2Rh4PCDet99GPQKvI5WCJBWJFB4wc9dduJ+REchIheoYuA5gZQXram3Fd7tdJmGxkXM68V48LsHezUqQusfP5kUppP9JImojot8lon9NRL+xISvS2BBwlgwT48wMPNBwGET1q78KQuR2x8kkyGrHDvHkAgEh7p/+VLzqm2+GVxyLgfiHh0Gyi4vSD6i5GYZnPR5tNiu7BCLxMOvrYXASCdzbxYuSLdPcjNexGN5bWZEMpLXWEIngHsPh4qePhcOyE+rrwxcXdzFBRqNicLk1RTFpmu9FzVz3+Nm8KCWQu0JEPHn1jzZmORobCY8HGTCDg2hf4Hav3pbffDN+drtBNN3d0POJ8LvOTsgVXi+6a3Z3Y+ZsbS0+PzKC90dHcd7HH0esIJWCgTCnLRYLs27MHT+jUXjivK5UCgbm+HHINl4vjFVHh/Te374dnr/LtTqf3wqtrdLGuZROpeEwWjeMj8PILC1JZXBVFdbR3g7jWYwcshGaebmMiO7xs3lRTHGWubvmv/yadJfNTQnud8M92c3bco9HGqVxxStrtrnweonuvx/Hv/oqPOILF0DyXV0gvh/8AB4vFxKVuvU368bnz2PK1tQUXj/8MNEnPgFDYhgIqCoF6WR5GQT86KPQ1jnQ2dtLdOYMzltZaZ262dmJTKTaWhiQYqt5Wc4ZGyP6xjewlltvxfoaGyWYzCMYc5+JVWuFcmvm5TYiusfP5kQxefq6u+YNhELb8mwWgVmPR95joiHCzy4XvP9UCgT72GPwokMhNHbr6ACZVlXBeHBzstytfzEeJ8simQzR178uMo5SaLhWXw/DU1uLddfX4+doFKmUXB/Q04M1+nzSf4jvo6ZGpoOtrEDa+cu/RDD6rrsQ0xgetl6fyyUVuOb6hjNnYKB+5VckhlJVBUNDBEPS0iLPxIqMeYJYuTTzjQq8bua4xFaF7rK5xVBoW54vP56raa3ey2bhzVZVyZAPro51uXDedHr11r9Yj5NJMRjEMTxZq6oK1+EBKMkk5KOlJUnDdLnwuXAY1+rrA8F/8pMg82QSn3G7YZi+8x1cx+cDMU5OIivJ67V+jl4v8vEnJiCX5WJ4mOhLX5J+QOk01hAKYZ2Vldh5EFmTcVdXeTXzzRx4fa/GNTYryv4IlVIOpdR3lFKvKKX+/vLvfk8p9bJS6idKKVe5r6lRGnhbzl9MBOZdANFqosl9jwmfvXiHAxp+OCyDT77wBfTOb28XOcnscfr9QrBWFcNsoPx+XCMaBVGHw3jd2IjXExP4/sYb6Id/9CgCySypXLokMQWnE4Td2Ej0/vejaVxdnaynoQGveQjMBz8oKasMpxO/6+lB4zWOe+QiFCL6+c9xnkRCisO4QCsYlElYPGvX6cRrLkRLp/GZXMNZKgr9276XEYshrsJfheonNIrDRnj6Hyeik4ZhPHGZ5PcS0S7DMA4qpf4/ImonojwbZo1rBatt+VrBOX5vaQmEVl8PQuVOj+3tRL/5m1KcpBS0/fXKFmxUdu0i+t3fJfqbv8H5nE4Ekg8ckC6WSiGtlHvZX7wIb9nvh07Pu4TaWnjcU1Mg3e5uaP/f/z7uK5vFerhffkUF0S//MiqQx8fx+cZG3D9nQLW1WQ9q4c6hvb0I4i4u4jl1dUkwd3oanytUiLZVA6+6FmBjsBGk/wwRPa2UchBRNRHtJ6IapdQhIpomor/OPUAp9SQRPUlE1MmJ1RrXBYWCc+bsn54eyZxhIuOcdA5E5vbPKUW2yJWAbr0VpD8zAyKvrcW5Ewl4wUQgX/aap6ag6/v94qmn09Ju2ecDybNB+I//kehP/xTBaKUQzN2zBxJRUxOO5xRRTk1tawNx7t2L3U1uOwe7HddZWJCeQbW1IHwiMXjNzdK51KoQrVwEt9kCr5tZknovo+ykbxhGhIhIKXWMiKaIqI6IZg3D+JhS6ggR3UNEL+cc8xUi+goR0b59+/Ik0mlcKxQimnzZP4YhEgVR/j9Yc/+cfB5nIQ+PdXCG0wlv++hReNI8xrCjA9W6LhcIurlZ4hYsoVRUyJrvuQejH0dHJR7w+uvQ+zMZZACtrMAz5+KrykpIQZOTuNbEhOT1885jYAD3t307rn/xIs7tdMJIcAdOv//akPFmCrzqWoCNQdlJXylVR6jevZuIXiCiMBGdu/z2MKHAS2OTotg/xNzPcQxAKeuumnwem610D8/nI7rzTnjpySSuc9990hUzlUK2jNNZ2NgEAujEmc1il1BVBQ89ncauwOOBQeCxiWw4Hn0U5/H7YRSSSUhev/EbMEQeD4ykzYbdAstjbAATCakoJtKBS8ZmlKQ2AzZC3vldIjpjGMbXlVIrRBQgooHL720jredvahT7h2j+nDkGMDoq2To2m3UmDw8hZ0PAHrGVh8dZQnv3QsZJp5E5U1MDQo7HQa5cDVyMvMGBVB6EwsFPHrL+2mtS2fv662hvsWsXJKXeXhihhx+WfvpssNiQZbMycjGTQayAx1fGYnhGbFA4CL4eI1DMWMX3OjabJLUZsBGk/z+J6GtKqd8koiEi+u9E9CWl1OtE9K5hGK9twDU1riGK/UPMjQGwpDE+jmAqUX4Zp7UV5D05KW0V2CM2g3cUTKipFAjUMGQoS1tbaRo5n9PhwLm4r04wiPbU0ajMyB0fxw5j/37ITF4vGsBVVoqGPzmJ451OxAeCQUkFTSRA8rxDGRqSNsl1dTCYVVUSL7FKb7Ui8xtprOJmkqQ2AzZC058gotxK3X9b7utoXF8U+4fIMQCuVuX0xaYmIel0GoFMlk5SKXi3LhcMAHvEVpkbVjuP3t7C3rGZJImsh4rnnvP226W3D/+Oj5mZEfmnqwuGwhyY5VkDHD8w72ImJvD7ykrsIi5ckKD4zAyMyt13yw4h9xnk2ymxMXW7YbSOH8e6olEY07ffJrr3Xkkh1dg60MVZGmUFN0cjApnZ7fDUJyZkiHg2K62Pjx9HZ87paRDdLbcg3//mm2UaFhGIKbdQjEk3387DiszMJJlMYm28TrP3m3vO5WXo+36/9MxfWcE5Mhl49FVViAtwYDYSwX3NzKw+PxuUSAT31Nsrxm5yEsaFaxKqq7FT8XpxDg74FmrVwMPlifDcl5chQ/34x0Tf+57EE269lej3fg8xic3i9WtcPTTpa5QNsRiknPFxEFJnJ7JamprQjI11+fZ2ENTgIOQS7oETicAIKAVi4rRQc8A43/jFYnYeZpJ0u3Eew4DUZLWT4HMePkz0138tDdSUQoDW6YRuz20oenulk2k2K7uX3JYWbFC4D7/DgXPOzuK80SjOEQ7LjICbbsK9h0L4LFH+gDeRGFqHAzUCc3NETz2Fc/Hx775L9Pd/j3Xfcov189sMcYDNsMb3EjTpa5QF2SykiTfeAFkRgWCIkLrY3Q2yYakmHAbpB4PwhkMhkKDPB+KamgKhJ5OrB6wXO37RCmaS5BbM3Po4X4bQ8jJ68SgFb97lgoe/d6+0djh4EAauv19SWTMZXMPhkFm25vNz2mtrKwrcWNLiWIDbLQPnk0k8L54ElkxKVa9VJhXHDs6dgxGZmoJx438XjlewEZmft86M2gyTsTbDGt9r0KSvURZwUDMclsDj8jJ0/G3bII1MTkK/Z7Ly+0FKo6Mgfu69n0pJJe22bTAEHKy9mmIdM0na7TJW0fy73AyhkRGQZlubBFmXlyU99IknpPFaKARDt7yMTJ/xcZzf40HmEstIDA4O89p6eiSo63TiWfG8g+5uBMGnpvBMuR9/vkwqrxcGlqWhlRVcJ5uVnUg2i7VXVl553xtdDVsO71xX7K4PmvQ1ygZzf3qWEBIJyAjsyXM1bTIJz93lEk9TKfwBT0+LNDE7C52c/4ivplgnN0BbW4trcpaPOfWUYxORCNYRiWBN6TSMWm8vCDmVwu7m9deJ3nwTRoKxezfRI49A/unoQO2A+fy5hJXJYPcwOAgpKRBAcVdbm+x+2tpA0maCs4pnGAayiSIRnKejAxPR3nhDKoebmtCeeufOK0lyI6thy+Wd64rd9UGTvkZZ4HRK1srSkuTGO5343fw80cmT0MezWenHX1ODYCU3XIvFxBNl48DSRzmKdawKw/KlOy4uQiJ53/uIfvITmYu7cyfWs7CA+zl5El49t4NgnD6Nr4YGon/zb5CFw7AiLLeb6MEHMQAmkcBrjgEwUXN7ZjPBOZ1XPgNuAeHz4d+iowPHjo3BMNntaBb32GPWDeM2qhq2nN65rthdHzTpa5QFNhukGG6AZhgIcmazSEWcmkKrhNlZ/JHb7QjiDgyAvFZWcIzLBVmislIKoFwuBEg9nvIU6+QGfXNbQPD6o1EQ4u7dIM1/+AcZwMI7kFOnQKRWXUIZs7NE//zPRA88AP3fPF8gl7ACAWQu5Rolc0C4GIKz2WBQ33pLjrn7bpl9zM3c8nUI3ahq2HJ657pid33QpK9RNng8IMgdO/DabgfhsxYdici23uGAZ/zOO5BYOOskmRR9PJuFjMLtE9gb3MhinUgEurrdDpmJi6n6+4k+9zmQ6MQEdiG9vZBiChE+IxTChLGGBpkmVoiwrKqVucCqGILLZvFZfmbZLP4d+vpWN8YrhI2ohi23d15ojTqrxxqa9DXKCnMzNiIUGl24AIKPxUBYXq9kn3CGjxnJJNGRI2hpPDMD4mUPsdQ/3lL+8Ll+wOmEZ+1yCfG3tkK++vSnQfzLyyCsH/+4uHWkUjAWQ0PSjM3txg6G6EqJxkoGsepmmg+5bR+yWXj4mczqf5+1UG4DuxHeudUadVZPfmjS19hQ+HyQNYjwBzg8DFJ3uyHjLC1ZHxeJQHvmnv2NjRIcLhal/uFzz532dkg8y8vQ5Acud44aGID8sn8/yHtsDDubsbG11+L1woAdPYr7b2uDfJVbSZtK4f1YDF+1tTg+XzdTM9JpePOctmm3I9g8MbF6UM22baURYLk95o3up6OzegpDk77GhqOykuihh+ClLi4SvfQSCNwwIHdYefvhMIK/58+DGCorpVlbMQ3I1vOHzzJDKITK1eeeA5FeuED0a78GT7u6Gue7+WZIJU4nyPvYMetz2mzYKdTUYKcQixGdPQsJrKoK9xKPSx//U6eQBcSjHD/wATy3tWSQUIjolVdkcPyePZCf3npLev3U1+OZcu+jYghwozzmjZTodFZPYWjS17gm8PlARH/0RxhyvrKC301OEn3zmwiampHJED3zDEjvwAE0H1MKHrbbXbgBGR9f6h8+tz7+xjegvwcCIOaFBaJ/+ie0LDAXVzkcIOQvf5noBz+AoThzBtepqADZNzRg11JTI2sIBGBM0ml44akUWi9UVyPGkUrBOCqF2b2f+hSOZxkkmwUZJ5NSzPbUUzAc8fjqIHhjoxhNmw3PmVtHlFLBvJk8Zp3VUxia9DWuGWw2eJi//MsgvIoKtCk+dQr548nk6s+fOIE/2Lo6GIldu6Cxd3eD7AqR0NX84TPJcSsELqIKhVYfz+erqUGQ90Mfghe/tITvkQhIeHERBsDthlHhQO74ODx6IhDs1BTI2u2GZ+7xCEF3dWFdsRiey6uv4hk2NOC9yUl48ZxKygalpwcGMp2GEeFAeTJZWlyAaPN4zDqrpzA06WtcU9hsIO3JSRBxTQ1SCYPB1YVNjHCY6Kc/BYHu2EH04Q8jk4aoMAmt9w/f45H6AI4hcNtjzpE3X6O5GTEHItzLJz6B3w8N4XfxOO7r3DkQdFsbdi4OB+Ib3HfI6ZSqWW7oxvUMvLPJZnGu11/HtZjgjxzBc+IBNZGIFI5xAP3SJRiYxUU8i7ffhhHdtSu/XJNrOM2DcIgKdyu93pkzug9/fmjS17jmMP9BtrVBcjh2zJr0L17EFxGCoD/6EXrhMHEW8t7X84fvcqHr5KlTKLwiQr3B449DljEbGHMbBSIYAM577+uDwXG50M3ygQekBoHTVbn3js8H8o/Hsd6TJ6W4bWBA2lCkUvhcOi3X8XpB/C0tME5zc/h9VxeIuqYGEs/yMjT/qip8pdOIl1RWyijHXJgNJw/Cqa1Fmmp9Pa67vIxnwJ/1+6/s3c+V19eafAvFDa6VUbrexs8KmvQ1rgv4D9LpBMmurMCTPH688HFzc0R/9VcgzIEB6Wq51nVKQWcn0Re+IG0TampA1ExcRNZ6dzAoUtNaBsfcJiEWgzHYv196/ExN4R47OiANcUA1FAKhcqfQVArHNDaCwBcWQOrNzSB+Ts90uaTxm8OBr2gUhsbcqjl3veZBOC0tOH8oRHToEIzMD36AOITTiQrfz39e5iB4PDAKx47BIHC/IPPO4nqQ4rVK5+TrmCev5SuGu5bQpK9x3cGE95/+EwKmMzMgpDNnpP2AGRcu4I/pwQc35o/VZpMe98EgiKm+frWBYb2bidduXy01pdMi13BdghncJoEDrJxLX1eHr1QKpMEVzJOTIPfubmT3PPUUyLWtjei3fgt5/6OjIsV0dOA58muWfjIZCSLzs7fbCxMh91RaWMD5QiHIQ4cPSypoMgmD7XQiZsP3ND8v1yZaHYO5Hrn01yo4zddJJhGHWlmB4Tx4EEb5ekKTvsZ1B3t5VVVE99wDT5e7dlqR/uIi/qiCQWS8EJVnhqwZnJa5fTte5xZP2e3S25/TT2tr8fv5eXjCZ8/iszfdhClVnHNPtFo6YaNhjjk4nasDvYYB75+Hv9x9N7x5w4Bx3LMH1zHfj8OxOqbx4IMga5Zjdu3CroaoMBHy7mZxETUJ0SiCxIuL+KzXizVx6unCgtQEcA8hXpN5EM71yAy6VsFpbh9++jT+Hbn1yMQE0S/8ApyI6wVN+hrXHTYbPNPJSUgJiQS8wIEBomefvfLzDQ0wENks5AOrIqe1jEAxXmZudXFu4JLJ3vw9nYbHOzYGyYUIHvjx49D1Haa/OJZOkkkZpsIw9+OPx3GPkQgId2oKu4SREXwuFAKRt7dLC2o+P0tMvMYHH5TMHW5ixx0+8xGhzYYdxZEjeN6cKsoB7nRanrPdjs+m01LsVlcn1+EYzPXKDLpW6ZxK4d9pZgZG0OfDs/r/2zv34Lav685/L8AXABJ8iKRISpRokbIsSlYs27Wt2E6s2LHrJLaykzpNu2nX29SZJtlmx5vpZrKTzqSzM51pOnXr5tVZu5luHo7rZOrYu2Otx44Tvx1bsiVb0sqSLImUSPEtkQQJggJw948vz14QBEiABEGQOJ8ZDqEff7i/e0Hxe88999xzJib4f+GOO2b/vvOJir5SEEgc/8GDtECNoZWdSvSbm7mp2tNDsZMMkpEIo2R8PlelK10h8WytzORJoqGBoiFVt7xeV0BdMoVWVLj3Slx94h/6fBPP9DR/BlDUAwG2f+ECx3jqFF8Lr79ON8+VV84t/xiJzD/Beb0uxbQUuUkUwnicr3fs4Gawz8dx9/fu7MvIAAAgAElEQVTTkg2HOa7mZuDeeznZbdzIa62t/B1JsffE1cxKxNLnK5xTDh4eOcJJu6aG45SxJv9fyCcq+kpBEI8zQuTKK/kHGAoBX/3q3PvKy2klBYO0kru7aUnX1dGqOnGC1zs7nXsjWcyztTJTTRL9/bOrbkklLtlglQRnAK1en8+5atK1KX0FKJQtLRzTpUuMYJqc5IG1Q4dm1y4AgF//mu1/9atMDTE9TbfC5s38fMrL009wkYibZIyh20hO7CZuRl68yD2FeNydJu7s5HNqatwp4MFBunw2buRkla5+8UrF0ucjnNPr5We0Ywf/X0tJzOrquf8X8o2KvlIQJAtxX58LmUwkEKDbx++nqB48SN95Ytm/1lYK32c+41w9yf74bKzMdJNEUxMFLhJhG9Zy9VFfzz90CTW96ipGtiS7b9JtBMvPKys5YYyOcpJ56y2KZLLgC4cO0bKUPk5OcsKQlUJzszvBmxix09vLZ23fzvdMTnLSjUadL9rv5/sPHaI419VR5D0eum/6+/kVCNC9kzzhphLWlYylX840ENL+xo0ubfjJk5wYGxpYanOlrHxARV8pEJKF+OzZuSd0AQr7pUsU/NOn+Xp8nNW5xsbcpmEkQvG9/fa5Yp6tlZlukqis5Nfly1xxSM57qXV7111s0++f+0cu7hSpECYRQrJ6mJpi/7q7XZ1gmWDSMTxMi7+3l+MZGeEZAWM4cfT1sQB6Yuhp4t5BOMzP8Te/ceGd4s8H+F388RLF09TECaC+3o1bUmQkT2KphH25xXc5WSjcNDEYIBrl5yLnNFYSFX2lIEgW4nShex4PhUWKhUsufq/XFVmR0MJjx4AbbnDtxePOKvd66XuenuYfYroNWxGlxL4ZQ4tNBFisdsl+KSuBQCB1RsxwmC4RyeRZX+9KMkqYak8P+zE0xPvF7z5fptF4nNE5o6MUd78fePll4O67nfusu5sRUiJS4gaKxTipHDniavM+95zbV6iooLi3tNCCveUW5kWqqnIb8ABXN7KCqK93EU5rLcVxpuGmEgyQTTrr5UZFXykYEpf79fX0hx49Ovue1lYeltqzh5aTJCbz+13StnCYLgfxnZ4/T/F94QXm+Onv58TQ30+BbWujK+iTn6RQDw/PTejm8/HZ3d3cN/jtb9mG10u3R3k5/eBSv1ZWB8kTSDRKH7tMXj4fN2RranjaWA6pGcNJqaODfTh0aGHRLyvjs0Ih7gVUVrpTulu20J/c2OgOal2+zA1hKVojReBLSoBf/coJPuBWHr29DKnt6eH4P/95jkUidaR/xjjXUVlZ+v2EbOsdFMLp1tWaiE5Q0VcKCrGs6+qAhx8GHnyQ1q8xFMBvfpM5eCorKfwTE/R1V1RQOCVX/9at9OsPDbHQySOPUGxTceoU0yhv3sykabIhuX69S0M8MgL86EfA44/TCvZ6+Yzqan5vbKQwbtjAvrW20nI+fpziLukPLlxwk1BvL4UzFAL+7d+4SSuRP5WV3Ky+6SZa7cPDdNX4fAz5++ADd/hLIpVktSPhmNZyEjtyxK1srHWbtnKyduNGTprr1vHgV1fX7PQSycjp4zffZJz/nj28LtE6AMc3OurCO+Va4n5CNoezCqkoSi7CTVdyAlPRVwqW22+nf/mFFygo27ZRnKJR/rHU1TEnzq5ddEV0d7uIlZ07acG//Tbw6KPpBT+Rri6eCP7ZzyhUHR3A7/0ei4f/6EfAP/8zhT0SofUaDvOQ1KlTXHGcPs1nV1ZS/J96iv2urATuucdt/g4Pu7C9gQHWF3jjDef/lvMH09PMASSRMA0NFLsrrgCeeYZJ1IaH+XmUlXGS2rGD7ZSVcdK46ipODk1NblP79Gm+r6uLwlxaymecP8/3XLo0t8h7IuIikxBVOTAm0T/l5ZxAJEIl1YZ5NtZyoVnWS431X+kJTEVfKWjq6uh2kT+SaHT2pmtJCYVtyxZa4OfPU3jGxrgK+OADilimjI3RIjeGLownn6Sgvfgi24nFXGZMgIIUDFJEx8YodA0NvK+5mX/Uly4Bf/M3tNq9XvZTVgBNTZw0RPATkc3qpiaK8/btvK+xEfjUp4Abb6Rw9/Tw+aWl7oh/SQldZBUVfJ/UGr54kZ9RRQX74fEwAurqq9m3LVvY1ugoVzepiMVcorbaWrbR1cXxyiGy0VG3d3DuHIVOcu9kciAs+XmFlOJ5KeGmhTCBqegrBU8moX1lZbQu/X7nRxZLN5uYaI/HReF4vRRy+RodnVsEPTEzaEkJRSAUcqF609OcqC5d4iZrunDLdFy+zAln3TquBhob+Vn80R9xVdHezmdFo5wc+vo4WYyMuAIwsucgFqkxLm+/ZM0cHHTumU2bGGKYTvTls+3s5OTR1eUS0kmU0PAwJ93WVv5OJNtmYs1eYzKzluV6JOI2iZd6kGup7pXFhpsWwgSmoq+sCjIN7UtOPXD5MnDddRTMixcXfr/fTyGTwujl5RS4sbG5gp9Mskukp2fh52WKpEzu76fL5+mn6dZqbwf27uVGdFUV3VIeDyebnh72v6/PpWOWNBeyMjh9muMrL2f8+LFj/Jlsjsu+gVBezpXM2Bj3QbZtY3oJr5eb26+9xo3ukRF+fu3twBe+4Iq5tLW5aJ7Ll52rTKxlwE0AgPs9VlZy3CKyV1+dWrgzEfNcuVcWE26azjUk/1fz4eNX0VfWHIl/jJ2d3Az++c/pZrCWlvA778x202zfzj2Ew4cZdjg9TR/53r3cQPX7V2Ys8/Huu/x68kmexL3uOu5DbN3qQkorKii0MhkmbuROTFDwW1p4bWSEX9Ho7KIuiZSUuKipri66g0ZH+ZmeOOHOS1RUuOpd+/cDX/qSc70FAs61MT3NlYXXyxXSmTMuR00sxudJYjefj5v1IyM8uBcIuHoEMmEsJOYr7V5J5RqqreVnmS8fv4q+sqbx+SjmN91EIQkEaOk99xzzyExPU/A/+1mKvETcTE/TlbJ5M63rbdvo+y5kDh5M38fKSm5unzzpVg2Ac7uI8J4+zQijdG6oiQl+VVfznv5+hpNWVjo3kwiqVO8aHORzZa8j+TxDJMK9l9de4+/m4kVuyk9NUQA7O10W1jfe4D2Tk+6cxY4dnFCA+cNDgcJwrySvRsU9lq9JSEVfWfNITLyUO6yqAn7/9xlRA8w+MbthA33giS6Cujrga19jiOKpUyszhqUSClEwk4nFaI1n64oaHXVnGiYmOGlIXpnxcVeTF+B9iS4zcWNMTXHiOHqUq4Fjxyh4U1O8X0pRSshnZyddQ8EgJ5JYjHsqpaWcACoq0oeHCvnKsrkQshrNZkM7Z8/OdYPGmBJjzM+NMa8aY36YcP1BY8zzuX6eoiyGkhKKRzA491i8xzM7f77HQx/yQw9x1aCQ3l7G+b/yCkV7YsJV8JKN3aYmRhpdcQXDTpubOQEdPUrrv7+f7/3gA5etU1YT8TjbGBykoMsewNQUfydy+G5ggJNPSYmrUpVOzMW9Eo26/EIrWTQ9cRIC8jMJLYel/2kAh6219xlj9htjrgFwEcD9AAaX4XmKsux4vbQ0/+qvKDLvvTf3HvEtnzuXuvjLWkWStD3/PDd1pcJXNMoVQEcH/dZeL8Vbom8A7q2cOcN7q6u5grCWAi6FYOJxrrY2bmSyve5ul4qjqoptrVvHCUXyE80XRrnSRdOTN5vznW10OUT//wB4xhhTAqAGwBiAhwF8A8B/WYbnKcqyI3+c8Tg3hv/2b7lpKbS0ALfdxs3UoSEeKDt1arb/XNqpr3cbl+PjeR3GstLXBzz7LPdB4nFa/TLOgwcZbXTiBN04kQi/P/ss/fEi3C0t9P2LFb9lC993881sp6qKG8WyqdvRQfdcMOgS4GUi5vlK9JYs8Okih/I5CeVc9K21IQAwxvwWwAUANwE4DOBYuvcYY74I4IsAsEnqtylKgSHFQ9avZ03fd97h11VXAbt38w94dJT3fvrTtPjPnqVISerhxkYK2XvvOeE7fnzhcNDVghzOGhigiIfDnNjq6ujjP3OGhXE8HqaTGBpyVr2Ekd57L+sqbNrE911xhas81dbGTfXjxxnVU1o6t37xfKKZz/QHyQIv5yjSbdrma8VhbLanRRZq0Jh1AEIAogBeAHAlgA/ACWYbgL+01n433fuvv/56e+DAgZz2SVFyjYiHlCEUEUm83tXlTt9eusRQw44OClgs5nLdPP00o4nWCpJjv6KCAr13L0WuoYE/e/NN5gN6+WWXPru8nFb6hz/MSKrrrqObyO+f/fkmFnWJxznRBoOZCWYu0x8sNHnE45zgSkrchnE4zJ8Fg+6+UIiinyoba7YYYw5aa69f6L7lcO98DcAxa+1PjDGTAL5grX3GGNMG4NH5BF9RVgvpLLPE6+Kr3bWLArFpkyuhGI3S6rvlFt537bXAP/6jE4bVQHl56vz+1roNyf5+hoG2trocQB6PC4sVJHTzwAGXVkPCP8Nhuno2b+YKItFSHh6eLaLpyGV8fiaTR6rQUPndrnTk0HKI/vcA/NgY8xXQwn92GZ6hKAXPfL7a8nL3s507uQF6ww08WbsaKCujZe71zj3EZS1dWh4Prfv2doaElpZSpMPh2QfjErlwAfjFL7gqkr2Bixe5ctq1i240KTifTXhjruLzM508UoWGlpY6F0++S0Qmshw+/R4AH0tx/SyAO3L9PEUpZObz1Sb+rKYGuPVW5qf/yU/y17/FEo26vEKpiMddiuhwmK6ahgZG3rz77vx1Ac6cYaZTv98VlLeWLqMHHgDuv98JbqaWcq7i8zOdPFJF5TQ1uRVLossq3+jhLEUpEPx+CsX69XSL5JvycoZWNjVxA3piwsWPJxOPU/BTZQcV/H62tWED26mtZfTN1JSrGpYOCQNNpL8f+Id/oJW9bx/bytRSzlVoZDaTh89HgZ+c5Arnrbe4n1NTw43nXPjxF4OKvqIUCD4fcOedTFwmefLzyQ03UJhHRlze/6Gh9CkZpqbmt5T9fo5Jiqt7PHT1XHkl00JHozyUlQ2Tk8yj1NHBKJ9sNmJzERqZzeQRDjM6a/9+flnLCf2eezj+7dtXKDV0/h+pKEoqPB5Gr3z968Dv/i5DEfNFWRnw0Y/SZz4+zs3Rqqr5E835fC7tQTJSSKW+nm0GAhTJ/n5eu+UW4I//mJE6EmufCRUVFNreXkZHRSLZhbsmn7aeDykpmdy+TB7ylWriicfpyjp8mBZ+aanb0/jlL3kiOd0qarlRS19RCgifD7jrLoYsHjgAfOc7LKOYiFTmOnEi+/z86SgrY5tDQ07EPB5nyUs4qiCiHgzyJG1fn4vGkUNVnZ0ubcLVV7Pt0VFX47inh5b/HXewQtqBAy6XjzxDzj0AdOvIJDM+zvEbw4lAImhyFYe/UITOQnH1sRjHEQqxTxLRVFrK8N3u7pU7m6GirygFhIhWfT2t/d27KfrPP08r+TOfoYUsMesnT3Ji+OlPl/bc3bspUrt2UewOHeL1ujq3GTs56bJYSnrknTudMI6MsP9+Pw9R3XwzN217eijQbW1ufFK0JRqluH/0o0wJPTDASUJEsaeHE8f4uEvLfN11/D4xQcH3eNhniYxZahx+LsI7p6YYiTQ6ys8uFHJpqQOBlfPnAyr6ilIwpLIum5tZJeu++1zBcxEen4++4c5ORrT84AcssL4YWlposdfUMILI73epkaem6Ju+dMklQgsEGI1TWso+BoMuX35bG9sbGKBVHw670MuqKgq1bILW1/NrZITx+dXVnGRGR9mXaJQTx/g4vweDfHYwSAE9e5afi9/PySExV/9i4/CXGt45MUGXjtfL1dO2bVzJVFTQp3/77VzhrFSSNxV9RSkA5rMuo1EKb2VlaqGoqmKc/8038z3f/jbTKE9P8xBUJuzfz2yYzc0U0x07KHa9vcATT1BcxdJvaKCAlZTw2VdfTdGtq6MYl5a6YjU7d7IPxvBnzc0U5g0b3Gpl82b658vL3YEvv5+H2Twed+q5vd0J8oUL/N7fzz5OTlJcd+92/vOJCU4WgcDcTKrzsZTwznic6TeM4XmCmhqO4/rr2VZtLftTX6/RO4pS1KSzLt9/H3jmGZdjft8+bnwmI6mG29uZAvrcOQrQgQPAX/8125mPSISCe+6c2xwtK2OhmXPn2DdrXUWr6mparaEQLfiREbfJev31bMPa2fH65eUUw3ic45OCKuXlFP7eXvr9h4Zc7d7WVoY39vZSQIeGXPy+5DMqKeHKYGiI7qA77+REeeSIm0yuuYZtZsJSwjslhFVqEkspyN27+fuJRnktMVdQvsl57p2lorl3lGIkVa6WUIiCX1np0g5PTgJf+YpL5zBfe+Im6u9niocf/pAul1Q0NDAUUqzrsTG6JB55hNlEE8NHS0sp0tu3OzeFZLfs7KQ1u3EjJ4V33uH1UMi5ZDZupCgm+92jUQq9tZxIZOK48UbeIxPPqVOciI4c4diOHHGFWtrb6dsvLaWbqbaWE+bUFPCRjziLP5MN38VsCsvvMRrlpnSqMSzXoayVzL2jKEqWpLIug0G6aKqreU91NS3qUGhh0U+MLmluBr75TYZH/uu/8rRrd7e7NxjkRLJ+PcX44kWuDh5/PHXbkjzszBn2Z8sWulF8Prpdqqooxq2tnBx8PueiCYednz1Z/GRlMDRE0ZaJ7tw5xvbLZFhSwrYPHaJlH4tR6EXcxf8eCvHZFRXs39QUxzc97TZ8jeG4U7nOFpP5MvH3KCuL1lYXdbRS1n0iKvqKUiAkHx6anqZgjY46AayooEBlSyDAAu/t7dwUfvNNWvB+P/Dxj7OGcF8fRfn+++kWmg8psSguodZWV8hkeprP6+11fuuSEpeczetN7WOfnmZb/f0cb20t2wfcJqr41mMxiqq0Mz7O1Uo0yjEFAnw9OMh+SSSQtexXc7OL+jl7lquCjRtzU5B8pYu0LISKvqIUEInWpfjwn3rK+cz37VvYyp+v7WCQ/uWdO52/XQ4rtbTw4FA23tVIhKuG3l62KZE769Y5C3xggKuHoSEK+ZkzvEf83XIGoK+P7iVxY0UibLO0dPZ5gaYmnlpubub9bW0u2+bEBF0pXi9rFgwP8zlSQxfg5PL222y/stLl+s9lQfJ85sfPFhV9RSlgOjroegmFKFCLFfxEPB5nQSfi81GQF0M0SvGPRGg5BwIU7/5+iur58xTfyUlOAocPc9VRWUkLu6TEbRBfey0FeHzcTRyJAlpW5vYCNm2iuIdCdNNI6Gk0SqGXSCGAVv74OMfY3e0+hw0b6D6amso8LDOfxVhyjYq+ohQ4FRW5EftMuPHGxb/30iW6Z86fpyulsZEVxuRngQBF8pVXKPzbtlFsp6f5WsIk/X66asJhXuvrm73h6/U6t1F9PV1TErIZi/H5XV28p7PTFW7p6aGwv/UWI4Hq6tiWnBSW6KOFyGUxlpVARV9RlP/PrbfS0n777ezfKwewvF73/cQJ5hO6fJkuH4n3r62lld/dTXHu6HAboOEwN4Tb2rgpnKqsYPKmd0cHVwAArX/ZAPd4+PrUKbYVCnFs4+P8eUsLJ4vOTrYficwv4LksxrJSrJJuKoqSD8rKmNJh/frFvV9cPBKTPjhIS72qiqd6z51zVn9lpTvxG4+7DdBNmyjGVVVsU2L6E9M4p0p6JgnSvF5a7XL/2Bh99uvWsUTj6Ch9/xcvMvFZVxdTQFRVUcDny4kj/ZCVRqq+FToq+oqizGLzZuCLX1zce4eGKPLd3bSqJSd/PM7QTqkXIOUih4Yo0ufP08L3eFyahkiE7xVrPtn1krgJLSGkZ85QxGtr+YxQiPc0NHDV0d9Py19SWsRi3OwdGXFRQfMJeOJpXWDlSh4uBRV9RVFmUV7OMM6tW7N/byRCa/7kSQr/5cs8DSzhnddeS0EeG6PQX3MNUz6UlTkr2+NxUT7Hj/N7be38h6jE5VJZye8XL7rTvO3trBXw/vvM3x8O82t83NWufe89nj4eHmZ76ax9cS3JhBKNzn9aNzE9c7pUzflGffqKoszC46EQf+hDFO/FMjrqEqKVldHqHxqiEF91FQU3GHRJ5ORQFUDRFj+57BXU1KQW11QpLEZHWZBdMnaePUthTxTccNhl7vzxj9nH0lLgT/+Uk1O6DdpM4/Blw1dcWLKKWenNX7X0FUWZQ2kp/fFLxVqXPjkcZpteLy3yYJCCGIvNdpOISEriNHHDJLtdxHI2ZrbLJRLh5BKLcaVw7Bjw2mv8nszkJJ8FuEnliSd4bT7//kLFWGT1Iad/332X6SKM4Upkob2D5URFX1GUOUSjdH8sFYlnD4UYLVNfTytXCqLEYq74uYRpdnczekdcMan85sk+/OpqXhsbo+jX1Tnhj8ddgZdUdHW5Nisr2dexsaVt0MZi/Pzee4+iHwq508CZ7B0sJyr6iqLMIRDghu5SKSujpe7zuZTN4TCFdmCAm7q1tby3t5cnbWMxuk+McS6ahgbXZrIPX3Lui+W8YYOL3pFsl/NFI01OUqA9HrqgJBxzKRu0xnDTWA6cGcOopXjc1RZeqc1fFX1FUeZQVsaUD0vB7+dm6u7dLI+4dSvF1+ulz3zHDor20aNOaI1hJE15OaN96uoonH19tOrD4dlhk/E4N1/FN+/zcTKRU7h+P5/R2Jh6EvN4GKo5NOQStt1xB9vKNJ1yKqQIutdLK7++nhPR1BR/tpS2l4pu5CqKkpK772Zs+/Bw9u9taeFJ2bvu4uGn6Wlu4La2MmonGOR9Hg8tcUmmJkVUxC0zOkrhF4u9t5finezDl1TNsiFcUcHTxefOMY1DTw999o8/zhO51vKepiZOFJs3s4hMaytTMAeDSxNlr9cVmBkc5Gqktpa1Bpba9lJR0VcUJSWNjcB3v8usm5IhMxM2bAD+4A8YodPW5qpfbdhAy1tSJIulnvh93ToKdDjMturrXZ4gid0XS1kiY6zlxCATiLhOAgGmeYjFmLht61bW4n3oIYZnymZyLEarfM8eTlCSBnkpJJ4abmzktcQUyyuJFlFRFGVeBgaAJ58Evv99+s7no6UF+LM/Y/x9RwetXXFpSMGV5Nw1tbUMyZR/NzXRvWQMff+JhWWi0dmhnLHY7Pz484VDynNPnwYee4yppT0eTghf/jKLwufaAs9nYrZMi6io6CuKsiDxOBOlfe5zjKxJx0MPAXv3UuAHBtILcbIYphPHTJObZSquct/lyy66Z/16rkBWO1o5S1GUnOHxMGPmt74FfP3rjERJ5oEHgE98grlzfD4XkplKiJPzzafLP5/pQahM89fLfaWl7GcxoqKvKEpG+Hw8rbpvH/DSS8CLLzKevbWVG7bXXDO77GCuCokUckGS1YiKvqIoGePx0B1y3338Ws3FRIoVFX1FURaNWuGrD/11KYqiFBEq+oqiKEWEir6iKEoRoaKvKIpSRKjoK4qiFBEFdyLXGDMIoCsPj6oHMJSH5xQSxTbmYhsvUHxjLrbxAunHvNla25Di+iwKTvTzhTHmQCZHltcSxTbmYhsvUHxjLrbxAksfs7p3FEVRiggVfUVRlCKimEX/f6x0B1aAYhtzsY0XKL4xF9t4gSWOuWh9+oqiKMVIMVv6iqIoRYeKvqIoShFRFKJvjCk1xvyvhH8bY8z/NMa8YYx52hhTkvCzB40xz69MT3NHJmOe73NYbWQ43gpjzP82xhw2xvzYGGNWss9LJXnMqa4ZYwLGmKeMMa8aY76d/17mlkzGPHPtvxpjXjbG7DfGlOW3l7kj0/HOXM9Iu9a86BtjfAAOAvh4wuWbAZRYa28CEARw58y9mwHcn+8+5posxpzyc1htZDHezwM4b639EIDapPtXFanGnOZz+PcA3rDW3gxghzFme147mkMyHbMxZguAHdbaWwHsB7Axz13NCVn8jrPSrjUv+tbasLV2F4DzCZf7ATw883o64frDAL6Rr74tF1mMOd3nsKrIYrwfA/DczOsXAOzNTw9zT6oxp/kcIgD8M6uaCqyx33OaMd8OoNYY8xKAWwGcyW9Pc0MW4wWy0K5Vu5xPhzHm+wB2JVx6yVr73xLvsdaenLn33wEoA/CsMeYPARwGcCxffc0Vix2ztTaWfC0/PV4aix0vgP8MYHTmljEA25a/t7khkzGn4TEArwO4D8CvrLUfLEf/loMljLkBwKC19l5jzOsAbgHw8nL0MZcsdrzZateaE31r7Zczuc8Ycy8oAvdYa2PGmE8B2ATgLgDbjDH/yVr73WXsas5Y7JjTXSt0lvA7HgJQPfPjaqyinC2ZjjkF3wDwT9baR40xPzPGfNha+1ou+7ZcLGHMYwDen3l9GsCG3PRoeVnCeLPSrjXv3kmFMaYJwF8A+KS1dhwArLV/aK29BcDnABxcLYKfKanGnOraWiHN2H4Ft2/xMQC/Xom+5ZkqAFMzryMAKlewL/niIIDfmXndAQr/miVb7SpK0QfwHwA0g26dV4wxf7LSHcoDqca8lj+HVGP7KYANxph3AYyAk8Ba53sAvjTj5vChCMZsrX0dwJAx5i0A71tr31zpPhUSeiJXURSliChWS19RFKUoUdFXFEUpIlT0FUVRiggVfUVRlCJCRV9ZsxhjfpPDtr6T9O82Y8xtWbz/NmPMt3LVH0VZLCr6ipIB1to/T7rUBuC2/PdEUZaGir6y5jHGlM+cRn3VGPOYMabMGHO/MebvjDEvGmP+rzFmx0xmzseMMW8ZY54wxvx9Qhu/SXj9IIDvAPiTmTMADYmW/Mwq4F9mXv/AGPMaeDJYsn8+MvO+XxhjvPn7JBRFRV8pDh4AcGwm0+RJAP9x5voeMFvhtwDsA1ADoN5a+zsAOqy1D6ZqzFr79wD+HMAPrbW3WGsHU91njLkBQIu19sMA3p65vA9A6cwJym4An8zB+BQlY1T0lWKgE0w6hpnvnTOvH7PWToMZOcsAhAGUz5xe/ekSnueb+b4FTIQFAHIqdBuAPTMrh48AWL+E5yhK1mMX/38AAADbSURBVKjoK8XAUQA3zby+aebfABBKuu8GAL+01u6x1v7dAm2GAQQAumzAlMWS1+bume9dAHbOvL525vv7AB631t4G4GsAjmc1EkVZIir6SjHwKFhA5FUAVwL4lzT3HQfwF8aYl4wxvzTG3DpPm++AGQ1fBfBZ0H1zjTHme2DOH8kBc2lm5SBtPQ2gxRjzCoD/jlWa611ZvWjuHUWZwRjzCQB/CWalHAfdP4+vbK8UJbeo6CuKohQR6t5RFEUpIlT0FUVRiggVfUVRlCJCRV9RFKWIUNFXFEUpIv4fnEdHhdj/owgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将alpha 设置为 0.1 ， 可以看出高密度数据点的位置\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', color='b',alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11519d9e8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们的大脑非常善于从图片中发现模式，但是需要玩转可视化参数才能让这些模式凸显出来\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "    sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "# 导入图片\n",
    "california_img = mpimg.imread('./california.png')\n",
    "# housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "#     s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "#     c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    "#     sharex=False)\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,\n",
    "    s=housing[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
    "    c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=False,\n",
    "    sharex=False)\n",
    "# 设置x,y轴的范围\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\", fontsize=14)\n",
    "plt.xlabel(\"Longitude\", fontsize=14)\n",
    "# 取出median_house_value房屋价格中位数，形成列\n",
    "prices = housing[\"median_house_value\"]\n",
    "# 分成十一等分\n",
    "tick_values = np.linspace(prices.min(), prices.max(), 11)\n",
    "# 取出颜色模板\n",
    "cbar = plt.colorbar()\n",
    "# 通过生成式提取颜色模板\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values], fontsize=14)\n",
    "# 设置lable\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "\n",
    "plt.legend(fontsize=16)\n",
    "# save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从图片中告诉我们，房屋价格与地理位置靠海，和人口密度息息相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 计算出每对特征之间的相关系数， 称为皮尔逊相关系数 (-1（负相关）,0(无关),1（正相关）)\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas-排序函数sort_values() 降序排序\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 收入和房价的线性关系是最大的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 相关系数范围 从 -1 变化到 1，越接近1 ，表示越强的正相关，，比如，当收入中位数上升时，房价中位数也趋于上升，\n",
    "   * 当系数接近-1 ，则表示有强烈的负相关，纬度和房价中位数之间出现轻微的负相关，越往北走，房价倾向于下降，\n",
    "   * 系数靠近0 ，说明二者之间没有线性相关性，\n",
    "   * 相关系数仅检测 线性相关性 ，如果x上升，y上升或者下降，可能会彻底遗漏非线性相关性，例如 如果x接近于0，y上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x117394438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11749cf60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x115101518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1176a0ac8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1212520b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114ecd668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114feac18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114fa1240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x114fa1278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11503fd68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114f78ba8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x114f6a908>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x114f0feb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117e624a8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x117e94a58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1183eb048>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用padans scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "# 对角线不看\n",
    "from pandas.plotting import scatter_matrix\n",
    "attributes = ['median_house_value','median_income','total_rooms','housing_median_age']\n",
    "scatter_matrix(housing[attributes],figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 16, 0, 550000]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter',x = 'median_income',y = 'median_house_value', alpha = 0.1,color = 'b')\n",
    "plt.axis([0,16,0,550000])\n",
    "\n",
    "# 大于50万的不看（设置了上限） 50万美元是一条清晰的线，被设置上限了， 45w，35w，28w，也有虚线， 可能是异常值，我可以在后期把数据删除掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "   2. 某些属性分布显示出了明显的“重尾”分布，对进行转换处理，计算其对数\n",
    "   3. 在给机器学习算法输入数据之前，最后一件事儿，尝试各种属性的组合\n",
    "   4. 每个项目历程都不一样，大致思路都相似"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试验不同特征的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把看似相关性不大的特征进行组合\n",
    "# 房间数在整个房子的的比例\n",
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "# 卧室数在所有房间的比例\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "# 家庭人口和房子的比例\n",
    "housing['population_per_households'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value           1.000000\n",
       "median_income                0.688075\n",
       "rooms_per_household          0.151948\n",
       "total_rooms                  0.134153\n",
       "housing_median_age           0.105623\n",
       "households                   0.065843\n",
       "total_bedrooms               0.049686\n",
       "population_per_households   -0.023737\n",
       "population                  -0.024650\n",
       "longitude                   -0.045967\n",
       "latitude                    -0.144160\n",
       "bedrooms_per_room           -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习算法的数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>286600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>340600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>196900.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>254500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "17606       710.0       339.0         2.7042            286600.0   \n",
       "18632       306.0       113.0         6.4214            340600.0   \n",
       "14650       936.0       462.0         2.8621            196900.0   \n",
       "3230       1460.0       353.0         1.8839             46300.0   \n",
       "3555       4459.0      1463.0         3.0347            254500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "17606       <1H OCEAN          2  \n",
       "18632       <1H OCEAN          5  \n",
       "14650      NEAR OCEAN          2  \n",
       "3230           INLAND          2  \n",
       "3555        <1H OCEAN          3  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到一个分层抽样代表全局数据集的训练集和测试集\n",
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集的数据和标签分开\n",
    "housing_lables = strat_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop('median_house_value',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "17606       710.0       339.0         2.7042       <1H OCEAN          2  \n",
       "18632       306.0       113.0         6.4214       <1H OCEAN          5  \n",
       "14650       936.0       462.0         2.8621      NEAR OCEAN          2  \n",
       "3230       1460.0       353.0         1.8839          INLAND          2  \n",
       "3555       4459.0      1463.0         3.0347       <1H OCEAN          3  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据清洗¶\n",
    "   * 大多数机器学算法无法在缺失的特征上工作，创建一些函数辅助，total_bedrooms有缺失\n",
    "   * 放弃这些区域\n",
    "   * 放弃这个属性\n",
    "   * 将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4629</th>\n",
       "      <td>-118.30</td>\n",
       "      <td>34.07</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3759.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>2.2708</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6068</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.01</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>5.1762</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17923</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>4.6328</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13656</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1.6675</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19252</th>\n",
       "      <td>-122.79</td>\n",
       "      <td>38.48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3468.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.1662</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "4629     -118.30     34.07                18.0       3759.0             NaN   \n",
       "6068     -117.86     34.01                16.0       4632.0             NaN   \n",
       "17923    -121.97     37.35                30.0       1955.0             NaN   \n",
       "13656    -117.30     34.05                 6.0       2155.0             NaN   \n",
       "19252    -122.79     38.48                 7.0       6837.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "4629       3296.0      1462.0         2.2708       <1H OCEAN          2  \n",
       "6068       3038.0       727.0         5.1762       <1H OCEAN          4  \n",
       "17923       999.0       386.0         4.6328       <1H OCEAN          4  \n",
       "13656      1039.0       391.0         1.6675          INLAND          2  \n",
       "19252      3468.0      1405.0         3.1662       <1H OCEAN          3  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)] # 返回一个True和False的矩阵 筛选出axis = 1 列为空的数据\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这两个方法不推荐\n",
    "#rows.dropna(subset = ['total_bedrooms']) # 删除为空的样本  放弃这些区域\n",
    "#rows.drop('total_bedrooms',axis = 1) #放弃这个属性\n",
    "\n",
    "#将缺失值设置为某个值，（0，平均数，中位数都可以）\n",
    "# 创建一个imputer实例，指定你要用属性中的中位数值替换该属性的缺失值 \n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "# 使用fit() 方法将 imputer实例适配到训练集\n",
    "housing_num = housing.drop('ocean_proximity',axis=1)\n",
    "imputer.fit(housing_num) # 估算器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.51  ,   34.26  ,   29.    , 2119.5   ,  433.    , 1164.    ,\n",
       "        408.    ,    3.5409,    3.    ])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-121.89  ,   37.29  ,   38.    , ...,  339.    ,    2.7042,\n",
       "           2.    ],\n",
       "       [-121.93  ,   37.05  ,   14.    , ...,  113.    ,    6.4214,\n",
       "           5.    ],\n",
       "       [-117.2   ,   32.77  ,   31.    , ...,  462.    ,    2.8621,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-116.4   ,   34.09  ,    9.    , ...,  765.    ,    3.2723,\n",
       "           3.    ],\n",
       "       [-118.01  ,   33.82  ,   31.    , ...,  356.    ,    4.0625,\n",
       "           3.    ],\n",
       "       [-122.45  ,   37.77  ,   52.    , ...,  639.    ,    3.575 ,\n",
       "           3.    ]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.20</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "17606    -121.89     37.29                38.0       1568.0           351.0   \n",
       "18632    -121.93     37.05                14.0        679.0           108.0   \n",
       "14650    -117.20     32.77                31.0       1952.0           471.0   \n",
       "3230     -119.61     36.31                25.0       1847.0           371.0   \n",
       "3555     -118.59     34.23                17.0       6592.0          1525.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "17606       710.0       339.0         2.7042         2.0  \n",
       "18632       306.0       113.0         6.4214         5.0  \n",
       "14650       936.0       462.0         2.8621         2.0  \n",
       "3230       1460.0       353.0         1.8839         2.0  \n",
       "3555       4459.0      1463.0         3.0347         3.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns = housing_num.columns, index = housing_num.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           16512 non-null  float64\n",
      " 1   latitude            16512 non-null  float64\n",
      " 2   housing_median_age  16512 non-null  float64\n",
      " 3   total_rooms         16512 non-null  float64\n",
      " 4   total_bedrooms      16512 non-null  float64\n",
      " 5   population          16512 non-null  float64\n",
      " 6   households          16512 non-null  float64\n",
      " 7   median_income       16512 non-null  float64\n",
      " 8   income_cat          16512 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info() #没有空值了 都是16512"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scikit-Learn\n",
    "* 一致性\n",
    "\n",
    "   1. 估算器 比如：各种机器学习算法 fit()执行估算器\n",
    "\n",
    "   2. 转换器\n",
    "      比如：LabelBinarizer transform() 执行转换数据集 fit_transform() 先估算，再转换\n",
    "\n",
    "   3. 预测器 predict() 对给定的新数据集进行预测 score() 评估测试集的预测质量\n",
    "   \n",
    "\n",
    "* 检查\n",
    "   1. imputer.strategy_学习参数通过公共实例变量访问"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19480</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8879</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13685</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "17606       <1H OCEAN\n",
       "18632       <1H OCEAN\n",
       "14650      NEAR OCEAN\n",
       "3230           INLAND\n",
       "3555        <1H OCEAN\n",
       "19480          INLAND\n",
       "8879        <1H OCEAN\n",
       "13685          INLAND\n",
       "4937        <1H OCEAN\n",
       "4861        <1H OCEAN"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7276\n",
       "INLAND        5263\n",
       "NEAR OCEAN    2124\n",
       "NEAR BAY      1847\n",
       "ISLAND           2\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本转化成对应的数字分类\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder() # 生成转换器\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat) #先调用fit再调用transform\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_\n",
    "# '<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN' 代表0，1，2，3，4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 机器学习算法会以为两个相近的数字比远的数字更相似，比如0，4 比  0，1相似度更高，\n",
    "   2. 创建独热编码，当 <1H ,第0个属性为1，其余都为0， 列别是InLand时候，另一个属性为1，其余为0，1为热，0为冷，独热编码\n",
    "   3. OneHotEncoder编码器, fit_trans需要二维数组， 转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import  OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [4],\n",
       "       ...,\n",
       "       [1],\n",
       "       [0],\n",
       "       [3]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoder.reshape(-1,1))  \n",
    "housing_cat_hot  # 读热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从稀疏矩阵转化成numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次完成两个转换 文本--整数类型--独热类型\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出稀疏矩阵\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output=True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# 通过列表生成是找到索引值\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\", \"population\", \"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix \n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X, y = None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household,population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household,population_per_household]\n",
    "        \n",
    "        \n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1   2     3     4     5     6       7           8  9       10  \\\n",
       "0 -121.89  37.29  38  1568   351   710   339  2.7042   <1H OCEAN  2  4.62537   \n",
       "1 -121.93  37.05  14   679   108   306   113  6.4214   <1H OCEAN  5  6.00885   \n",
       "2  -117.2  32.77  31  1952   471   936   462  2.8621  NEAR OCEAN  2  4.22511   \n",
       "3 -119.61  36.31  25  1847   371  1460   353  1.8839      INLAND  2  5.23229   \n",
       "4 -118.59  34.23  17  6592  1525  4459  1463  3.0347   <1H OCEAN  3  4.50581   \n",
       "\n",
       "        11  \n",
       "0   2.0944  \n",
       "1  2.70796  \n",
       "2  2.02597  \n",
       "3  4.13598  \n",
       "4  3.04785  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17606</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>37.29</td>\n",
       "      <td>38</td>\n",
       "      <td>1568</td>\n",
       "      <td>351</td>\n",
       "      <td>710</td>\n",
       "      <td>339</td>\n",
       "      <td>2.7042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.62537</td>\n",
       "      <td>2.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18632</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>37.05</td>\n",
       "      <td>14</td>\n",
       "      <td>679</td>\n",
       "      <td>108</td>\n",
       "      <td>306</td>\n",
       "      <td>113</td>\n",
       "      <td>6.4214</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00885</td>\n",
       "      <td>2.70796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14650</th>\n",
       "      <td>-117.2</td>\n",
       "      <td>32.77</td>\n",
       "      <td>31</td>\n",
       "      <td>1952</td>\n",
       "      <td>471</td>\n",
       "      <td>936</td>\n",
       "      <td>462</td>\n",
       "      <td>2.8621</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22511</td>\n",
       "      <td>2.02597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>-119.61</td>\n",
       "      <td>36.31</td>\n",
       "      <td>25</td>\n",
       "      <td>1847</td>\n",
       "      <td>371</td>\n",
       "      <td>1460</td>\n",
       "      <td>353</td>\n",
       "      <td>1.8839</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.23229</td>\n",
       "      <td>4.13598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>-118.59</td>\n",
       "      <td>34.23</td>\n",
       "      <td>17</td>\n",
       "      <td>6592</td>\n",
       "      <td>1525</td>\n",
       "      <td>4459</td>\n",
       "      <td>1463</td>\n",
       "      <td>3.0347</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.50581</td>\n",
       "      <td>3.04785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "17606   -121.89    37.29                 38        1568            351   \n",
       "18632   -121.93    37.05                 14         679            108   \n",
       "14650    -117.2    32.77                 31        1952            471   \n",
       "3230    -119.61    36.31                 25        1847            371   \n",
       "3555    -118.59    34.23                 17        6592           1525   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "17606        710        339        2.7042       <1H OCEAN          2   \n",
       "18632        306        113        6.4214       <1H OCEAN          5   \n",
       "14650        936        462        2.8621      NEAR OCEAN          2   \n",
       "3230        1460        353        1.8839          INLAND          2   \n",
       "3555        4459       1463        3.0347       <1H OCEAN          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "17606             4.62537                   2.0944  \n",
       "18632             6.00885                  2.70796  \n",
       "14650             4.22511                  2.02597  \n",
       "3230              5.23229                  4.13598  \n",
       "3555              4.50581                  3.04785  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加列名属性\n",
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\", \"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()\n",
    "\n",
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>-116.54</td>\n",
       "      <td>33.82</td>\n",
       "      <td>12</td>\n",
       "      <td>9482</td>\n",
       "      <td>2501</td>\n",
       "      <td>2725</td>\n",
       "      <td>1300</td>\n",
       "      <td>1.5595</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>7.29385</td>\n",
       "      <td>2.09615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6263</th>\n",
       "      <td>-117.96</td>\n",
       "      <td>34.04</td>\n",
       "      <td>34</td>\n",
       "      <td>1381</td>\n",
       "      <td>265</td>\n",
       "      <td>1020</td>\n",
       "      <td>268</td>\n",
       "      <td>4.025</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.15299</td>\n",
       "      <td>3.80597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12208</th>\n",
       "      <td>-117.1</td>\n",
       "      <td>33.56</td>\n",
       "      <td>6</td>\n",
       "      <td>1868</td>\n",
       "      <td>289</td>\n",
       "      <td>750</td>\n",
       "      <td>247</td>\n",
       "      <td>4.3833</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.56275</td>\n",
       "      <td>3.03644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>34.14</td>\n",
       "      <td>44</td>\n",
       "      <td>1446</td>\n",
       "      <td>250</td>\n",
       "      <td>721</td>\n",
       "      <td>243</td>\n",
       "      <td>4.7308</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>5.95062</td>\n",
       "      <td>2.96708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12601</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>38.53</td>\n",
       "      <td>37</td>\n",
       "      <td>1704</td>\n",
       "      <td>361</td>\n",
       "      <td>902</td>\n",
       "      <td>356</td>\n",
       "      <td>1.9837</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.78652</td>\n",
       "      <td>2.53371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13354</th>\n",
       "      <td>-117.61</td>\n",
       "      <td>34.02</td>\n",
       "      <td>15</td>\n",
       "      <td>1791</td>\n",
       "      <td>346</td>\n",
       "      <td>1219</td>\n",
       "      <td>328</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.46037</td>\n",
       "      <td>3.71646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5749</th>\n",
       "      <td>-118.27</td>\n",
       "      <td>34.16</td>\n",
       "      <td>45</td>\n",
       "      <td>1865</td>\n",
       "      <td>360</td>\n",
       "      <td>973</td>\n",
       "      <td>349</td>\n",
       "      <td>3.6587</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.34384</td>\n",
       "      <td>2.78797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18799</th>\n",
       "      <td>-121.89</td>\n",
       "      <td>40.97</td>\n",
       "      <td>26</td>\n",
       "      <td>1183</td>\n",
       "      <td>276</td>\n",
       "      <td>513</td>\n",
       "      <td>206</td>\n",
       "      <td>2.225</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.74272</td>\n",
       "      <td>2.49029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15022</th>\n",
       "      <td>-117</td>\n",
       "      <td>32.77</td>\n",
       "      <td>30</td>\n",
       "      <td>1802</td>\n",
       "      <td>401</td>\n",
       "      <td>776</td>\n",
       "      <td>386</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.66839</td>\n",
       "      <td>2.01036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16834</th>\n",
       "      <td>-122.55</td>\n",
       "      <td>37.59</td>\n",
       "      <td>31</td>\n",
       "      <td>1331</td>\n",
       "      <td>245</td>\n",
       "      <td>598</td>\n",
       "      <td>225</td>\n",
       "      <td>4.1827</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.91556</td>\n",
       "      <td>2.65778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>-121.99</td>\n",
       "      <td>37.96</td>\n",
       "      <td>17</td>\n",
       "      <td>2756</td>\n",
       "      <td>423</td>\n",
       "      <td>1228</td>\n",
       "      <td>426</td>\n",
       "      <td>5.5872</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.46948</td>\n",
       "      <td>2.88263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14906</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.6</td>\n",
       "      <td>33</td>\n",
       "      <td>905</td>\n",
       "      <td>205</td>\n",
       "      <td>989</td>\n",
       "      <td>222</td>\n",
       "      <td>2.7014</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07658</td>\n",
       "      <td>4.45495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10779</th>\n",
       "      <td>-117.91</td>\n",
       "      <td>33.65</td>\n",
       "      <td>17</td>\n",
       "      <td>1328</td>\n",
       "      <td>377</td>\n",
       "      <td>762</td>\n",
       "      <td>344</td>\n",
       "      <td>2.2222</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.86047</td>\n",
       "      <td>2.21512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7934</th>\n",
       "      <td>-118.08</td>\n",
       "      <td>33.82</td>\n",
       "      <td>26</td>\n",
       "      <td>4259</td>\n",
       "      <td>588</td>\n",
       "      <td>1644</td>\n",
       "      <td>581</td>\n",
       "      <td>6.2519</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.33046</td>\n",
       "      <td>2.8296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9745</th>\n",
       "      <td>-121.7</td>\n",
       "      <td>36.67</td>\n",
       "      <td>37</td>\n",
       "      <td>641</td>\n",
       "      <td>129</td>\n",
       "      <td>458</td>\n",
       "      <td>142</td>\n",
       "      <td>3.3456</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.51408</td>\n",
       "      <td>3.22535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18768</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>40.51</td>\n",
       "      <td>23</td>\n",
       "      <td>2216</td>\n",
       "      <td>378</td>\n",
       "      <td>1006</td>\n",
       "      <td>338</td>\n",
       "      <td>4.559</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.55621</td>\n",
       "      <td>2.97633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5564</th>\n",
       "      <td>-118.29</td>\n",
       "      <td>33.91</td>\n",
       "      <td>41</td>\n",
       "      <td>2475</td>\n",
       "      <td>532</td>\n",
       "      <td>1416</td>\n",
       "      <td>470</td>\n",
       "      <td>3.8372</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.26596</td>\n",
       "      <td>3.01277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7064</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>33.94</td>\n",
       "      <td>30</td>\n",
       "      <td>2572</td>\n",
       "      <td>521</td>\n",
       "      <td>1564</td>\n",
       "      <td>501</td>\n",
       "      <td>3.4861</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.13373</td>\n",
       "      <td>3.12176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13637</th>\n",
       "      <td>-117.32</td>\n",
       "      <td>34.08</td>\n",
       "      <td>41</td>\n",
       "      <td>1359</td>\n",
       "      <td>264</td>\n",
       "      <td>786</td>\n",
       "      <td>244</td>\n",
       "      <td>2.5208</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.56967</td>\n",
       "      <td>3.22131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4827</th>\n",
       "      <td>-118.32</td>\n",
       "      <td>34.03</td>\n",
       "      <td>31</td>\n",
       "      <td>2206</td>\n",
       "      <td>501</td>\n",
       "      <td>1194</td>\n",
       "      <td>435</td>\n",
       "      <td>1.9531</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.07126</td>\n",
       "      <td>2.74483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "12347   -116.54    33.82                 12        9482           2501   \n",
       "6263    -117.96    34.04                 34        1381            265   \n",
       "12208    -117.1    33.56                  6        1868            289   \n",
       "6396    -118.03    34.14                 44        1446            250   \n",
       "12601   -121.48    38.53                 37        1704            361   \n",
       "13354   -117.61    34.02                 15        1791            346   \n",
       "5749    -118.27    34.16                 45        1865            360   \n",
       "18799   -121.89    40.97                 26        1183            276   \n",
       "15022      -117    32.77                 30        1802            401   \n",
       "16834   -122.55    37.59                 31        1331            245   \n",
       "1468    -121.99    37.96                 17        2756            423   \n",
       "14906   -117.06     32.6                 33         905            205   \n",
       "10779   -117.91    33.65                 17        1328            377   \n",
       "7934    -118.08    33.82                 26        4259            588   \n",
       "9745     -121.7    36.67                 37         641            129   \n",
       "18768   -122.24    40.51                 23        2216            378   \n",
       "5564    -118.29    33.91                 41        2475            532   \n",
       "7064    -118.03    33.94                 30        2572            521   \n",
       "13637   -117.32    34.08                 41        1359            264   \n",
       "4827    -118.32    34.03                 31        2206            501   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "12347       2725       1300        1.5595          INLAND          2   \n",
       "6263        1020        268         4.025       <1H OCEAN          3   \n",
       "12208        750        247        4.3833       <1H OCEAN          3   \n",
       "6396         721        243        4.7308          INLAND          4   \n",
       "12601        902        356        1.9837          INLAND          2   \n",
       "13354       1219        328        3.8125          INLAND          3   \n",
       "5749         973        349        3.6587       <1H OCEAN          3   \n",
       "18799        513        206         2.225          INLAND          2   \n",
       "15022        776        386        2.8125       <1H OCEAN          2   \n",
       "16834        598        225        4.1827      NEAR OCEAN          3   \n",
       "1468        1228        426        5.5872          INLAND          4   \n",
       "14906        989        222        2.7014      NEAR OCEAN          2   \n",
       "10779        762        344        2.2222       <1H OCEAN          2   \n",
       "7934        1644        581        6.2519       <1H OCEAN          5   \n",
       "9745         458        142        3.3456       <1H OCEAN          3   \n",
       "18768       1006        338         4.559          INLAND          4   \n",
       "5564        1416        470        3.8372       <1H OCEAN          3   \n",
       "7064        1564        501        3.4861       <1H OCEAN          3   \n",
       "13637        786        244        2.5208          INLAND          2   \n",
       "4827        1194        435        1.9531       <1H OCEAN          2   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "12347             7.29385                  2.09615  \n",
       "6263              5.15299                  3.80597  \n",
       "12208             7.56275                  3.03644  \n",
       "6396              5.95062                  2.96708  \n",
       "12601             4.78652                  2.53371  \n",
       "13354             5.46037                  3.71646  \n",
       "5749              5.34384                  2.78797  \n",
       "18799             5.74272                  2.49029  \n",
       "15022             4.66839                  2.01036  \n",
       "16834             5.91556                  2.65778  \n",
       "1468              6.46948                  2.88263  \n",
       "14906             4.07658                  4.45495  \n",
       "10779             3.86047                  2.21512  \n",
       "7934              7.33046                   2.8296  \n",
       "9745              4.51408                  3.22535  \n",
       "18768             6.55621                  2.97633  \n",
       "5564              5.26596                  3.01277  \n",
       "7064              5.13373                  3.12176  \n",
       "13637             5.56967                  3.22131  \n",
       "4827              5.07126                  2.74483  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "# 房间总数 范围6到39320，收入中位数范围0到15\n",
    "# 目标值通常不需要缩放\n",
    "# 同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "# 最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数feature_range\n",
    "# 标准化 减去平均值 ，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "# 缩放器仅用来拟合训练集，不是完成的数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "# 调用流水芡的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "# 估算器，只会调用fit方法\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()  # 有缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame -> series -> ndarray（numpy数组）\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs = list(housing_num)\n",
    "# housing_num.head()\n",
    "# 取得所有列名\n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "# 自定义转换器 解决数据问题\n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn_features.transformers import DataFrameSelector\n",
    "# 解决文本信息问题\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "# housing.head()\n",
    "# 两个pipeline是同时执行的，内部的命令按顺序执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "# 合并pipeline\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调用pipeline\n",
    "housing_fin = full_pipeline.fit_transform(housing)\n",
    "housing_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.156043</td>\n",
       "      <td>0.771950</td>\n",
       "      <td>0.743331</td>\n",
       "      <td>-0.493234</td>\n",
       "      <td>-0.445438</td>\n",
       "      <td>-0.636211</td>\n",
       "      <td>-0.420698</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_fina = pd.DataFrame(housing_fin)\n",
    "housing_fina.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "   * 获得了数据\n",
    "   * 数据探索\n",
    "   * 对训练集和测试集进行拆分\n",
    "   * 编写了转换数据流水线\n",
    "   * 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "# 调用流水芡的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "# 估算器，只会调用fit方法\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('imputer', SimpleImputer(strategy = \"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 17606 to 15775\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16354 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs =list(housing_num) \n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(num_attribs)),\n",
    "        ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin \n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),               \n",
    "        ('LabelBinarizer', MyLabelBinarizer()),\n",
    "    ])\n",
    "\n",
    "# housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipline\", num_pipeline,),\n",
    "        ('cat_pipline', cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.156043</td>\n",
       "      <td>0.771950</td>\n",
       "      <td>0.743331</td>\n",
       "      <td>-0.493234</td>\n",
       "      <td>-0.445438</td>\n",
       "      <td>-0.636211</td>\n",
       "      <td>-0.420698</td>\n",
       "      <td>-0.614937</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.312055</td>\n",
       "      <td>-0.086499</td>\n",
       "      <td>0.155318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.176025</td>\n",
       "      <td>0.659695</td>\n",
       "      <td>-1.165317</td>\n",
       "      <td>-0.908967</td>\n",
       "      <td>-1.036928</td>\n",
       "      <td>-0.998331</td>\n",
       "      <td>-1.022227</td>\n",
       "      <td>1.336459</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.217683</td>\n",
       "      <td>-0.033534</td>\n",
       "      <td>-0.836289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.186849</td>\n",
       "      <td>-1.342183</td>\n",
       "      <td>0.186642</td>\n",
       "      <td>-0.313660</td>\n",
       "      <td>-0.153345</td>\n",
       "      <td>-0.433639</td>\n",
       "      <td>-0.093318</td>\n",
       "      <td>-0.532046</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.465315</td>\n",
       "      <td>-0.092405</td>\n",
       "      <td>0.422200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.017068</td>\n",
       "      <td>0.313576</td>\n",
       "      <td>-0.290520</td>\n",
       "      <td>-0.362762</td>\n",
       "      <td>-0.396756</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>-0.383436</td>\n",
       "      <td>-1.045566</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.079661</td>\n",
       "      <td>0.089736</td>\n",
       "      <td>-0.196453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.492474</td>\n",
       "      <td>-0.659299</td>\n",
       "      <td>-0.926736</td>\n",
       "      <td>1.856193</td>\n",
       "      <td>2.412211</td>\n",
       "      <td>2.724154</td>\n",
       "      <td>2.570975</td>\n",
       "      <td>-0.441437</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>-0.357834</td>\n",
       "      <td>-0.004194</td>\n",
       "      <td>0.269928</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.156043  0.771950  0.743331 -0.493234 -0.445438 -0.636211 -0.420698   \n",
       "1 -1.176025  0.659695 -1.165317 -0.908967 -1.036928 -0.998331 -1.022227   \n",
       "2  1.186849 -1.342183  0.186642 -0.313660 -0.153345 -0.433639 -0.093318   \n",
       "3 -0.017068  0.313576 -0.290520 -0.362762 -0.396756  0.036041 -0.383436   \n",
       "4  0.492474 -0.659299 -0.926736  1.856193  2.412211  2.724154  2.570975   \n",
       "\n",
       "          7         8         9        10        11   12   13   14   15   16  \n",
       "0 -0.614937 -0.954456 -0.312055 -0.086499  0.155318  1.0  0.0  0.0  0.0  0.0  \n",
       "1  1.336459  1.890305  0.217683 -0.033534 -0.836289  1.0  0.0  0.0  0.0  0.0  \n",
       "2 -0.532046 -0.954456 -0.465315 -0.092405  0.422200  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -1.045566 -0.954456 -0.079661  0.089736 -0.196453  0.0  1.0  0.0  0.0  0.0  \n",
       "4 -0.441437 -0.006202 -0.357834 -0.004194  0.269928  1.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_prepared)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 获得了数据\n",
    "* 数据探索\n",
    "* 对训练集和测试集进行拆分\n",
    "* 编写了转换数据流水线\n",
    "* 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17606    286600.0\n",
       "18632    340600.0\n",
       "14650    196900.0\n",
       "3230      46300.0\n",
       "3555     254500.0\n",
       "           ...   \n",
       "6563     240200.0\n",
       "12053    113000.0\n",
       "13908     97800.0\n",
       "11159    225900.0\n",
       "15775    500001.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_prepared)\n",
    "df.head()\n",
    "#取出中位数\n",
    "housing_lables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# 初始化估算器 机器学习的线性回归算法\n",
    "lin_reg = LinearRegression()\n",
    "#           训练数据            标签\n",
    "lin_reg.fit(housing_prepared, housing_lables)\n",
    "# 模型的对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [203682.37379543 326371.39370781 204218.64588245  58685.4770482\n",
      " 194213.06443039]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "# 取少量数据测试取（0:5）\n",
    "some_data = housing.iloc[:5]\n",
    "# 取到对应标签\n",
    "some_labels = housing_lables.iloc[:5]\n",
    "# 将测试数据（some_data）放入流水线中(full_pipeline)\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "# 预测模型对象前五条数据（some_data_prepared）\n",
    "print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68376.64295459939"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE均方根误差\n",
    "from sklearn.metrics import mean_squared_error\n",
    "#预测结果\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "#真实结果\n",
    "lin_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "#平方根误差\n",
    "lin_mse\n",
    "#均方根误差\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n",
    "# 大多数地区的房屋中位数 在120000到265000美元之间，预测误差高达 68628,这是一个典型的模型对训练数据拟合不足的案例，\n",
    "# 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\n",
    "# 1. 选择强大的模型，2 为算法提供更好的特征，3.减少对模型的限制等方法，\n",
    "\n",
    "# 决策树可以找到复杂的非线性关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=42, splitter='best')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "# 估算器\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "# 训练训练集和它的标签\n",
    "tree_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到预测误差\n",
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "#                                真实回归          预测误差\n",
    "tree_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "# 计算误差\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse\n",
    "# 过拟合  训练集拟合的效果很好 但是对于训练集不一定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 完美，也可能是这个模型对数据严重过度拟合了，如何确认？轻易不要启动测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70274.7991723 , 67258.3624668 , 71350.42593227, 68882.91340979,\n",
       "       70987.99296566, 74177.52037059, 70788.57311306, 70850.53018019,\n",
       "       76430.62239321, 70212.6471067 ])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个（验证集）折叠进行评估，另外9个进行训练\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "# 决策树交叉验证\n",
    "#                        决策树     测试集              标签             均方差                        交叉几次\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_lables,scoring=\"neg_mean_squared_error\", cv=10)\n",
    "# 求平方根（多次的交叉验证）\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores\n",
    "# 拿到10次的值，每一次的均方差误差比线性效果差，也就说明之前得到的0.0 没有代表性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [70274.7991723  67258.3624668  71350.42593227 68882.91340979\n",
      " 70987.99296566 74177.52037059 70788.57311306 70850.53018019\n",
      " 76430.62239321 70212.6471067 ]\n",
      "Mean: 71121.4387110585\n",
      "Standard deviation: 2434.3080046605132\n"
     ]
    }
   ],
   "source": [
    "# 把每次的误差打印出来\n",
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66866.97628534 66611.30659392 70575.91118868 74177.09497875\n",
      " 67683.32205678 71132.78742458 64782.65896552 67704.33562155\n",
      " 71080.40484136 67686.67641836]\n",
      "Mean: 68830.14743748441\n",
      "Standard deviation: 2665.5767413568587\n"
     ]
    }
   ],
   "source": [
    "# 线性 交叉验证                线性模型\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_lables,scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)\n",
    "# 决策树没有得到比线性更好的选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "#                                  集成了多棵的树\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "#              训练集             标签\n",
    "forest_reg.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22107.0888400092"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_lables, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51481.61843757 48867.18698326 53592.93924497 54917.35753217\n",
      " 50463.39403515 56776.52132037 51940.08691385 50521.84102811\n",
      " 55729.5024898  53136.5656865 ]\n",
      "Mean: 52742.701367175265\n",
      "Standard deviation: 2412.0829538615826\n"
     ]
    }
   ],
   "source": [
    "# 再一次进行交叉验证\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#                               森林数据\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_lables,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参和网格搜索\n",
    "   1. 手动调整超参数，找到很好的组合很困难\n",
    "   2. 使用GridSearchCV替你进行搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# 网格搜索\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "]\n",
    "# 初始化随机森林模型算法\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# 将随机森林放入网格中，没有调用fit                                                             返回训练分数\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,scoring='neg_mean_squared_error', return_train_score=True)\n",
    "# 将数据和标签\n",
    "grid_search.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 访问全局的参数，找到效果最好的参数\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features=6, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=30, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 找到最好的估算器（模型） 显示所有参数\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64246.880700613496 {'max_features': 2, 'n_estimators': 3}\n",
      "55869.85367924813 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.1282558969 {'max_features': 2, 'n_estimators': 30}\n",
      "61376.36445082522 {'max_features': 4, 'n_estimators': 3}\n",
      "53846.329115303764 {'max_features': 4, 'n_estimators': 10}\n",
      "51270.1941502407 {'max_features': 4, 'n_estimators': 30}\n",
      "59860.61532587693 {'max_features': 6, 'n_estimators': 3}\n",
      "53114.42460001889 {'max_features': 6, 'n_estimators': 10}\n",
      "50811.43543872171 {'max_features': 6, 'n_estimators': 30}\n",
      "59220.31563298743 {'max_features': 8, 'n_estimators': 3}\n",
      "52884.78697544277 {'max_features': 8, 'n_estimators': 10}\n",
      "50944.39369116168 {'max_features': 8, 'n_estimators': 30}\n",
      "62805.52917192821 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54462.1410888642 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "61117.32056104296 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "53022.992252269294 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "60234.58052562756 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52712.989031117104 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    #打印所有结果对应的参数 （第九条最好对应6和30）\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加你不确定的特征，比如是否使用转换器 combinedAttre的超参数add_bedrooms_per_rom\n",
    "# 也可是使用它自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100,\n",
       "                                                   n_jobs=None, oob_score=Fals...\n",
       "                                                   warm_start=False),\n",
       "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12309bdd8>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x124fb3208>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "         # 随机数 范围1：200\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49931.87687086762 {'max_features': 7, 'n_estimators': 180}\n",
      "52122.359500383354 {'max_features': 5, 'n_estimators': 15}\n",
      "51463.975706036916 {'max_features': 3, 'n_estimators': 72}\n",
      "51381.480933769904 {'max_features': 5, 'n_estimators': 21}\n",
      "50040.68374785571 {'max_features': 7, 'n_estimators': 122}\n",
      "51436.83595589131 {'max_features': 3, 'n_estimators': 75}\n",
      "51286.420458183085 {'max_features': 3, 'n_estimators': 88}\n",
      "50312.65330988299 {'max_features': 5, 'n_estimators': 100}\n",
      "50995.48957108733 {'max_features': 3, 'n_estimators': 150}\n",
      "65455.451908645155 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "# 查看最好的结果（第一条）\n",
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.79326113e-02, 6.18280724e-02, 4.33395023e-02, 1.81017027e-02,\n",
       "       1.83291556e-02, 1.93269892e-02, 1.78369580e-02, 2.41360490e-01,\n",
       "       1.61976585e-01, 5.35982558e-02, 1.06273526e-01, 6.14045141e-02,\n",
       "       1.22353255e-02, 1.08821239e-01, 2.76143239e-05, 2.59938294e-03,\n",
       "       5.00807682e-03])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取到最佳模型的最佳特征值\n",
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.2413604895538288, 'median_income'),\n",
       " (0.16197658459849273, 'income_cat'),\n",
       " (0.10882123891274473, 'INLAND'),\n",
       " (0.10627352591969833, 'population_per_household'),\n",
       " (0.06793261134305181, 'longitude'),\n",
       " (0.061828072419167844, 'latitude'),\n",
       " (0.06140451407841603, 'bedrooms_per_room'),\n",
       " (0.05359825584988401, 'rooms_per_household'),\n",
       " (0.04333950231438805, 'housing_median_age'),\n",
       " (0.0193269891794112, 'population'),\n",
       " (0.018329155582427953, 'total_bedrooms'),\n",
       " (0.018101702689683707, 'total_rooms'),\n",
       " (0.017836957990116878, 'households'),\n",
       " (0.01223532548334132, '<1H OCEAN'),\n",
       " (0.005008076816921073, 'NEAR OCEAN'),\n",
       " (0.0025993829445232247, 'NEAR BAY'),\n",
       " (2.761432390218492e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总的所有特征名称（）\n",
    "# num_attribs\n",
    "# 额外的三个特征    平均房间数量             卧室和人口的比例               卧室和所有房屋的比例\n",
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "# 列表之间数据的合并\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "# 将三种特征进行累加得到所有特征的名称\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "# 进行从大到小排序 数字越大重要性越高\n",
    "sorted(zip(feature_importances, attributes), reverse = True)\n",
    "# 继续优化特征 多次迭代"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过测试集评估系统\n",
    "   1. 从测试集中获取预测器（X）和标签（y）\n",
    "   2. 运行full_pipline来转换数据\n",
    "   3. 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49040.18609727207"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义最终模型 最好的模型\n",
    "final_model = grid_search.best_estimator_\n",
    "# 从测试集当中拿到x,y 通过分层采样\n",
    "# 删除房价的中位数，以列删除 \n",
    "# 输入数据，用来估算\n",
    "X_test = strat_test_set.drop('median_house_value', axis = 1)\n",
    "# 取得标签数据\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "# 直接通过流水线\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "# X_test_prepared\n",
    "\n",
    "# 转化成dataframe\n",
    "# df = pd.DataFrame(X_test_prepared)\n",
    "# df.head()\n",
    "\n",
    "#最终模型预测                               输入数据   \n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "#进行比对 用均方根误差\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "# 开个根号\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse\n",
    "# 现阶段得到最优的值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目启动阶段\n",
    "   1. 展示解决方案 学习了什么\n",
    "   2. 什么有用\n",
    "   3. 什么没有用\n",
    "   4. 基于什么假设\n",
    "   5. 以及系统的限制有哪些\n",
    "   6. 制作漂亮的演示文稿，例如收入中位数是预测房价的首要指标\n",
    "   \n",
    "## 启动，监控和维护系统\n",
    "   1. 为生产环境做好准备，将生产数据源接入系统\n",
    "   2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "   3. 时间推移，模型会渐渐腐坏，定期使用新数据训练模型\n",
    "\n",
    "## 评估系统性能 \n",
    "   1. 需要对系统的预测结果进行抽样评估，需要人工分析，分析师可能是专家，平台工作人员，都需要将人工评估的流水线接入你的系统\n",
    "   2. 评估输入系统的数据质量\n",
    "   3. 使用新鲜数据定期训练你的模型，最多6个月\n",
    "   \n",
    "## 总结\n",
    "    本周主要学习 数据准备，构建监控工作，建立人工评估流水线，自动化定期训练模型上，熟悉整个机器学习流程，\n",
    "## 建议\n",
    "    选择一个数据集，尝试从A到Z的整个过程，从竞赛网站上，选择一个数据集，一个明确目标，以及可以一起分享经验的同伴\n",
    "    https://www.kaggle.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
